bluuebunny/crossref_metadata_2025_split huggingface dataset:
RETRACTED: Research on the Application of Modern Dyeing and Weaving Art from Pattern Design to Dyeing Art based on Computer Technology in Mobile Environment
Authors: Yuan Guo 
Published: May 2021

Abstract
With the rapid development of Chinese society and economy, the position of traditional dyeing and weaving technology in textile art has been improved. Modern painting art is one of the manifestations of economic and cultural development in modern society, and it is also a new source of inspiration for modern textile pattern design. The integration of mobile Internet and mobile terminals in modern society has subverted the social structure, and a pluralistic, open and infinite society has brought opportunities and challenges to everyone and every industry. The intelligence and convenience of mobile digital media make it integrate into people’s lives and change the way of life of the masses. In order to ensure that people can provide better visual effects and experience, the use of modern painting art can effectively enhance the connotation and value of dyeing and weaving pattern design. In order to get further development of traditional dyeing and weaving technology, we must maintain the national style while improving and innovating its dyeing process. With the progress of modern science and technology, the combination of art and technology is more important.
Teaching Research Computer-Aided Design in Art Design Specialty
Authors: Song Xiao 
Published: May 2021

Abstract
In art design, the computer can help the designer to calculate, store information, draw and so on. In the design, computers are usually used to calculate, analyze and compare different schemes in order to determine the optimal scheme. All kinds of design information, whether digital, literal or graphic, can be stored in the computer’s internal memory or external memory, and can be quickly retrieved; Designers usually start with a sketch, and the heavy lifting of turning the sketch into a working drawing can be done by the computer. Computer can be used to edit, enlarge, shrink, translate and rotate graphics data processing work. Therefore, it is a teaching method that art design teachers must master to master computer technology to assist design. Based on this, this paper discusses the computer-aided design teaching of art design major.
Application of Intelligent Terminal Mobile Learning Platform in English Teaching
Authors: Sun Zhiyong , Shangguan Ting 
Published: May 2021

Abstract
Under the Internet+ operating model. The emergence of new teaching models has broken the traditional teaching methods, and a series of online teaching methods such as the Internet have provided children with newer teaching models. This not only facilitates the way of learning, but also brings more convenience to English teaching, and further promotes the method of English learning. Compared with traditional learning methods, it is more convenient to use the smart terminal mobile learning platform through the Internet. Teaching and other methods provide students with a better platform. Under the influence of smart terminals, such a learning platform can not only provide a diversified learning atmosphere, but also provide students with a better way of thinking, breaking conventions, and pioneering Innovation, and further enhance students’ interest in learning. And this kind of learning platform is more commonly used in English teaching [1].
Research on the Creation and Dissemination of Chinese Traditional Poetry under the Condition of Computer and Media
Authors: Wang Shoubo 
Published: May 2021

Abstract
China is a country with many excellent traditional cultures, which is the cultural precipitation of China’s 5000 year history. Therefore, we must strengthen the inheritance and dissemination of traditional culture. Among them, Chinese traditional poetry is the embodiment of Chinese traditional culture, which needs our continuous inheritance. Media is an important medium of inheritance, which plays an important role. With the popularity of the Internet, computer media has become the most important way, which has become the main way of communication. Therefore, we need to constantly strengthen the combination of traditional Chinese poetry and financial media. Through the integration of media technology, we can spread Chinese traditional poetry in a digital way, which will become a new communication ecology of information digital sharing. First of all, this paper analyzes the transmission mode of Chinese traditional poetry. Then, this paper analyzes the characteristics of digital communication in the financial media environment. Finally, some suggestions are put forward.
Research on the Coupling Simulation of Natural Ventilation and Building Thermal Model
Authors: Liu Yuxi 
Published: May 2021

Abstract
In order to reduce the implementation cost of building insulation, and more flexible to meet the needs of building regulation, we need to actively find new methods to carry out the research of building thermal model. Based on this, this paper first analyses the principle and organization of building natural ventilation, then studies the coupling simulation of natural ventilation and building thermal model, and finally gives the result analysis of the coupling simulation of natural ventilation and building thermal model.
Semantic Segmentation of Radar Detections using Convolutions on Point Clouds
Authors: Braun M. , Cennamo A. , Schoeler M. , Kollek K. , Kummert A. 
Published: May 2021

Abstract
For autonomous driving, radar sensors provide superior reliability regardless of weather conditions as well as a significantly high detection range. State-of-the-art algorithms for environment perception based on radar scans build up on deep neural network architectures that can be costly in terms of memory and computation. By processing radar scans as point clouds, however, an increase in efficiency can be achieved in this respect. While Convolutional Neural Networks show superior performance on pattern recognition of regular data formats like images, the concept of convolutions is not yet fully established in the domain of radar detections represented as point clouds. The main challenge in convolving point clouds lies in their irregular and unordered data format and the associated permutation variance. Therefore, we apply a deep-learning based method introduced by Point CNN that weights and permutes grouped radar detections allowing the resulting permutation invariant cluster to be convolved. In addition, we further adapt this algorithm to radar-specific properties through distance-dependent clustering and pre-processing of input point clouds. Finally, we show that our network outperforms state-of-the-art approaches that are based on Point Net++ on the task of semantic segmentation of radar point clouds.
An Improving Noise Immunity Algorithm for Multiple Kernel Boosting Using Noise Detection and Kernel Selection Technique
Authors: Tian Ye , Wang Mei 
Published: May 2021

Abstract
Focus on the problem that the Multiple Kernel Boosting(MKBoost) algorithm is sensitive to noise, a Multiple Kernel Boosting algorithm based on weight update and kernel selection is proposed. Firstly, the algorithm use the combined classification error rate of the previously selected classifier and the current classifier to be selected as the selection index of the kernel function in the weak classifier before the kernel of the base classifier is selected in each iteration; Secondly, in the weight update stage, a new weight update method is constructed by fusing the noise-detection and the average of weights in Multiple Kernel Boosting algorithm, which reduce the sensitivity to noise samples. Among the 8 of UCI data sets with varying levels of noise, the algorithm was compared with MKBoost-D1, MKBoost-D2, under the accuracy criteria, it performed better than traditional MKBoost algorithms. Experimental results show that the algorithm is able to effectively reduce the sensitivity of MKBoost to noise, and also has better robustness than traditional MKBoost algorithms.
Sound Event Detection Based on Convolutional Neural Networks with Overlapping Pooling Structure
Authors: Zhu Hang , Wan Hongjie 
Published: May 2021

Abstract
In this paper, a sound event detection measure is proposed. This measure is based on convolutional neural networks with overlapping pooling structure Different from the traditional GMM-HMM model and DNN-HMM model, the CNN model uses the convolutional layer which can speed up training by reducing training parameters. In this paper, the extracted sound feature is the mel-frequency cepstrum coefficient (MFCC). The dropout layer is added to the convolutional layer. Over-fitting can decrease the accuracy of the detection, dropout layer can prevent the model from over-fitting. Moreover, the overlapping pooling structure is used in CNN, the stride size is smaller than the pooling kernel size. The output of pooling layer has overlapping parameters, which can increase the richness of features. The final experimental results show that the precision of the proposed CNN model more robust than the GMM-HMM model and baseline model.
Design of the Servo Control System Based on EtherCAT P
Authors: Hua Qiang , Yao Yunchang , Zhou Weigang , Kong Lingyu , Xie Anhuan , Zhou Zhihui , Zhang Dan 
Published: May 2021

Abstract
EtherCAT is fully compliant with Ethernet standards, with 100Mbps transfer speed and good synchronization. It is widely used in the industrial field, such as the servo control system, robots, and numerical control machine tool. There are still lots of wires for power and communication in these applications, especially in the complex multi-axis system, leading to high installation cost and poor reliability. In this paper, a novel servo control system based on EtherCAT P is proposed. The EtherCAT P technology is an addition to EtherCAT, which can enable the standard 4-wire Ethernet cable to transmit not only data but also two electrically isolated power supplies. The hardware design and software architecture of the servo control system are described including servo motor controller, powered device, and power sourcing devices. A servo control system with one servo slave and TwinCAT master is built and some tests are carried out. The experimental results verify the power supply capacity, stability, and motor control functions.
Study on Control Strategy for Tilt-rotor Aircraft Conversion Procedure
Authors: Yan Xufei , Chen Renliang , Lou Bin , Xie Ye , Xie Anhuan , Zhang Dan 
Published: May 2021

Abstract
This paper studies the control strategy in tilt-rotor aircraft dynamic conversion procedure. A nonlinear flight dynamics model with full flight modes is established. On this basis, a nonlinear optimal control model of dynamic conversion is built by constructing the Bolza form of nonlinear optimal control problem. It contains the limitations and effects of conversion corridor, pilot control, flight attitude, engine rated power, wing stall, and the cooperation between lift and thrust on the procedure of dynamic conversion. An efficient numerical solution method with good convergence is designed to obtain the trajectory and control strategy of dynamic conversion between the modes of helicopter and fixed-wing aircraft. Through the weight parameter analysis, it can be seen that when the subitem of pilot workload is included in the cost function, the displacements of collective stick input as well as the longitudinal stick input are significantly reduced, and the height and pitch attitude change more gently, but the time has been extended. In addition, the pilot workload weight coefficient should not be dominant, otherwise the overly smooth manipulations of collective and longitudinal stick will cause a decrease of height.
A Multi-resolution Community Partition Method based on Node Degree
Authors: Xia Quan , Wu Fan , Chen Kebin 
Published: May 2021

Abstract
Community detection is one of the significant methods to study the properties of complex networks. Community resolution, as an important index of detection algorithm, is often used to evaluate the performance of the algorithm, but many classical algorithms have shortcomings in this respect. Based on this, this paper proposes a multi-resolution community partition method based on node degree. By introducing similarity coefficient and propagation coefficient of degree, the resolution of network is effectively controlled. Experiments on a scale-free network with 100 nodes show that the proposed algorithm is stable and efficient, with low time complexity. The number of communities can vary from 3 to 27 with different combinations of two coefficients, which can well discover the rules and characteristics of the network.
Design of Student Attendance System based on 2.4Ghz RFID Technology
Authors: Donger Zhou 
Published: May 2021

Abstract
In order to improve school management of student attendance, based on the 2.4Ghz card, using RFID technology and server back-end technology, A set of student entry and exit attendance system has been designed. The current positioning method generally used distance fitting model based on RSSI. This method has very high accuracy, but the accuracy of calculation results is highly affected by the environment and the amount of data required for calculation is very large. Usually, the data need to be transmitted from the terminal to the server for processing witch increases calculation complexity. The paper proposes an algorithm based on least squares semaphore, this algorithm greatly simplifies the calculation complexity and improves the calculation efficiency.
An Res-Unet Method for Pulmonary Artery Segmentation of CT Images
Authors: Liu Zhenhong , Yuan Hongfang 
Published: May 2021

Abstract
In the recognition of pulmonary embolism, the accuracy of pulmonary artery segmentation plays a key role. Due to the irregular shape of pulmonary artery and the complex adjacent tissues, it is very challenging to segment pulmonary artery using traditional convolutional neural network. Therefore, an improved Res-Unet method for pulmonary artery segmentation is proposed in this paper. To begin with, the U-net structure is used as the basis structure to allow efficient information flow. Secondly, in order to improve the gradient circulation of the network, our model introduces residual connections based on the U-net structure, that is, adding a connection from the input to the output of the two convolutions and performing a convolution operation. Finally, to quick converge, we use a hybrid loss function, which is linearly combined by Dice loss and Cross Entropy loss. The experimental results show that the proposed framework ranks higher than U-net on recall, precision and Dice, yielding results comparable to that of manual segmentation.
A Novel Terrain Adaptive Landing Gear Robot
Authors: Tang Hongyan , Tian Chunxu , Zhang Dan 
Published: May 2021

Abstract
In order to expand the landing areas and application range of vertical take-off and landing (VTOL) aircrafts, this article proposes a new type of landing gear robot. The robot is mainly composed of a base and three limbs with planar parallel mechanisms. The control system collects depth data through a depth camera, and fuses attitude angle and velocities to establish terrain data of the landing area. According to the terrain data and the robot kinematics model, the driving variables are calculated and used to drive the robot to achieve the adaptive landing.
Binary Quantum Elite Particle Swarm Optimization Algorithm for Spectrum Allocation in Cognitive Wireless Medical Sensor Network
Authors: Zhou Jie , Qin Hu , Zhang Yao , Yang Rui , Liu Yang , Li Chaoqun 
Published: May 2021

Abstract
Cognitive wireless medical sensor network (CWMSN) is a new development direction of the wireless sensor network (WSN) in recent years. The requirements of CWMSN are multi-faceted such as optimized utilization of resources, energy efficiency and channel sharing. However, CWMSN also faces the problem of limited energy and spectrum. For this reason, this paper proposes a spectrum allocation model. Moreover, a new binary quantum-behaved elite particle swarm optimization algorithm (BQEPSO) based on the combination of quantum operator, elite operator and binary particle swarm optimization (BPSO) is proposed to solve the spectrum allocation problem in CWMSN. The algorithm can enable the conflict-free use of spectrum resources in CWMSN and maximize the efficiency of spectrum allocation. The BQEPSO is compared with the genetic algorithm (GA) and traditional particle swarm optimization (PSO) under the same experimental conditions. The simulation results indicate that the performance of BQEPSO is better than the other two algorithms. And BQEPSO can maintain the total throughput of the CWMSN. What’s more, BQEPSO has a high network reward and throughput.
Research on Text Multi-Feature Fusion Algorithm Based on AM-CNN
Authors: Wensong Wang , Xiange Sun 
Published: May 2021

Abstract
How to know the changes of students’ emotions in real time in online education has always been one of the important issues concerned by education departments and teachers. Making use of the special functions of global, part of speech and positional attention mechanisms in text processing, a deep learning network model based on the combination of multiple attention mechanisms and convolution neural networks is proposed. Firstly, the blending characteristics between the types of attention mechanism and CNN are analyzed by using the standard ChnSentiCorp_htl_all data set to clarify the effectiveness of the combination of the three attention mechanisms and CNN. Then the model is applied to the analysis of the evaluation text of the course “big data Technology principles and applications” on the MOOC of Chinese universities, and it is verified that the evaluation indexes of this model are better than the existing conventional models.
Cloud Computer Research on Table Detection Model Based on the DC-LSTM Model
Authors: Zhang Xingming , Bai Yunchao , Wei Naike , Pan Huadong , Yin Jun 
Published: May 2021

Abstract
In view of the fact that tables are not easy to detect, this paper designs a table detection model based on the DC-LSTM module, which references the Convolutional Long Short-Term Memory (ConvLSTM). The model uses the backbone network of target detection to extract convolution features. The feature pyramid network is used to complete the detection task, and the DC-LSTM module is embedded in a special position in the feature pyramid network. In order to evaluate the performance of the DC-LSTM module, we added the DC-LSTM module to the YOLO v3, SSD network. Specifically, we added the DC-LSTM module to the YOLO v3 network, the new model can achieve an accuracy of more than 98% on the Table Bank data set and own data set. The model in this paper can realize the automatic extraction of table document images, which is of great significance for the realization of automated data collection.
An R-SIFT Image Matching Intelligent Algorithm Applied to Hardware Trojan Detection
Authors: Sun Chen , Liang Pujiang , Li Lingling , Ma Jingjing , Jiao Licheng , Liu Fang 
Published: May 2021

Abstract
Malicious modification, deletion or addition of some modules in the integrated circuits (ICs) will cause the chip to be attacked, which is called Hardware Trojans (HTs). Reverse engineering (RE) is a classic destructive method and widely used in HTs detection. However, RE is very time-consuming and error prone. In this paper, based on RE, we propose the R-SIFT algorithm to match image and reformulate the Trojan detection problem as change detection problem. For the R-SIFT algorithm, the ratio of exponentially weighted averages (Roewa) operator is introduced into the scale-invariant feature transform (SIFT) algorithm. Experiments show that the proposed method has 7 to 56 times more matching points than the original SIFT can be effectively applied to HTs detection.
A Container Deployment Optimization Method for Edge Computing
Authors: Zhang Jianfeng , Liu Bin , Wang Xiaochuan 
Published: May 2021

Abstract
In recent years, with the rapid development of Cloud Computing, Big Data and Internet of Things, the amount of data generated by edge nodes grows profusely, which results in high network bandwidth requirements. In order to improve the QoS of Cloud Computing, edge computing has received wide-spread attention in both industry and academia. As a resource isolation technology for edge nodes, container virtualization has been widely used. However, under the condition of unstable network connections in edge environments, there are some shortcomings on native docker container, such as large amount of image data transmission, network interruption and retransmission cost, which affect the quality of edge services. Therefore, this paper proposes an Images Distribution Method based on Deduplication (IDM-Dedup), which improves the transmission efficiency of container image and reduces the time to deploy and migrate edge services. The experimental results show that IDM-Dedup is more effective than native docker container method.
Research and Practice of Steam Demand Forecasting Model in Intelligent System for Industrial Enterprises
Authors: Qian Jie , Ye Zhihui , Ding Nanzhe , Sun Zhejian , Zhang Xun 
Published: May 2021

Abstract
Under the environment of China’s continuous promotion of energy conservation and emission reduction, the idea of on-demand energy supply of steam power system in tobacco manufacturing industry is put forward. Through the research and optimization of prediction algorithm, we analyze the historical data and energy supply process of steam energy, and establish the on-demand energy supply prediction model of steam energy, in order to realize the on-demand energy supply prediction of steam energy in tobacco manufacturing industry.
Research on Intelligent Analysis and Prediction Model of Funds Flow Based on Non-stationary Time Series
Authors: Yue Qiang , Hu Zhongyu , Li Dongping 
Published: May 2021

Abstract
Funds flow prediction is critical to the stable operation of financial service institutions. In view of the non-stationary characteristics of funds inflow and outflow in financial institutions, this paper introduces the key characteristics of time series analysis, compares AR, MA, ARMA and ARIMA models, and proposes a funds flow prediction model based on ARIMA algorithm. The stationary test, pure randomness test and model order determination of time series are explained in detail. Finally, combined with the error and score of the model, the relatively optimal model is selected to predict the test data. The experimental results show that, for non-stationary time series, the model has smaller prediction error and can effectively predict funds flow.