bluuebunny/crossref_metadata_2025 huggingface dataset:
A Review of Underwater Target Recognition Based on Deep Learning
Authors: Chen Yujie , Niu Haichun , Chen Huiwei , Liu Xiaoling
Published: April 2021

Underwater target detection is an important part of national defense, which is related to national defense security. Underwater target recognition technology based on deep learning has made great progress in recent years, but there are still some problems, such as the feature is not obvious, the contour is not obvious, and so on. This paper introduces the current situation of underwater target recognition based on deep learning, analyzes the commonly used deep learning methods, and provides a new idea for the further research of underwater target recognition based.
Epidemiological Characteristics of the Corona Virus Disease 2019 in Shaanxi Province Based on “Internet +” Epidemic Monitoring Cloud Platform
Authors: Zhang Hairui , Feng Yancheng , Ma Yonghong , Tan Jiao , Zhang Ye , Han Jiming , Men Ke
Published: April 2021

Since the outbreak of the Corona Virus Disease 2019(COVID-19), medical staffs have reported cases of hospital infection of COVID-19, which has greatly affected the physical and mental health of medical staff. In order to effectively avoid the immunization situation and prevent the front-line medical staff from collecting information and reporting on the new coronary pneumonia patients, suspected patients, close contacts and other personnel, the possibility of being infected due to various direct and indirect contacts may arise. Information technology, the establishment of a new cloud monitoring platform based on “Internet +” for coronary epidemic outbreaks, and the realization of “zero touch” operation and “paperless” management of the entire process of information collection, confirmation, review and reporting, which greatly reducing the front-line work. The infection risk and labor intensity of personnel have reduced the workload and improved the efficiency of information collection. At the same time, it has also realized automatic statistics, intelligent statistical description and synchronous sharing of the information.
An Energy Efficient Clustering Algorithm in Wireless Sensor Networks for Internet of Things Applications
Authors: Ci Xingjie , Wen Kang , Sun Ying , Sun Weifan , Deng Wei
Published: April 2021

Internet of things (IoT) integrates sensor, radio frequency identification, network and cloud computing technology into a large-scale monitoring area. Wireless sensor network (WSN), as the most suitable infrastructure for the Internet of Things, how to reduce its energy consumption has been a research hotspot. In hierarchical networks, cluster head nodes need to take on more energy consumption tasks. Therefore, reasonable cluster head selection scheme and normal node joining cluster mechanism become particularly important. This paper proposes a new clustering method to save energy and prolong network life. Firstly, in the cluster head selection phase, the influence of energy threshold, residual energy, node density and communication factor is considered. Secondly, in the ordinary node joining cluster phase, the residual energy and distance factor are considered to form the non-uniform cluster set of the network. Simulation results show that the algorithm has good performance in reducing and balancing energy consumption.
Logo Based on Improved Generative Countermeasure Network Image Super Resolution Reconstruction Method
Authors: Luo Pengcheng , Hu Gengsheng , Tan Zhiping
Published: April 2021

In order to solve the problem that the existing learning based super-resolution reconstruction methods rely too much on the degenerate model while the degradation model is unknown, which leads to noise and edge jagged in the reconstructed image, an image super-resolution reconstruction method based on improved generative countermeasure network is proposed. The generator of the network consists of up sampling module, denoising module and anti-aliasing module Firstly, the input image is sampled by the up sampling module to generate the initial super-resolution image; then, the denoising module and anti-aliasing module are used to reconstruct the clear super-resolution image; in order to reconstruct a better logo image, a joint training loss is introduced, including content loss and resistance loss, and the content loss includes perception loss and edge loss Lost. The experimental results show that, compared with the current super-resolution reconstruction method based on generative countermeasure network (SRGAN), the peak signal-to-noise ratio (PSNR) of the reconstructed image is improved to 0.2675dB and the structural similarity is improved to 0.035, which can effectively improve the quality of logo image reconstruction.
Research on Airport Taxi Problem Based on Cell Transmission Simulation Model
Authors: Li Haizhen , Wang Chunli , Qin Lijuan , Su Linda , Xie Chengjie , Yang Qunxing
Published: April 2021

In order to solve the decision-making problem of taxi drivers at the airport, it constructed a non-aggregate stochastic utility model, a cell transmission simulation model, and a queuing model. Besides, it used software programming and drawing software such as MATLAB, SPSS, EXCEL, and rapid flow chart comprehensively. Finally, this paper solves the taxi problem at the airport and gives relevant suggestions.
Application of UAV Target Tracking Based on Computer Vision
Authors: Wang Xudong , Luo Bo , Zhang Zhiyuan
Published: April 2021

With the progress and development of China’s reform and opening up and economic globalization, China has made great progress in science and technology, especially in computer vision technology. From the current development of our country, the progress and development of computer vision technology plays an important role in the development and utilization of UAV automatic navigation. With the continuous development of automatic navigation technology, artificial intelligence has been widely used in various fields, and computer vision technology as the main direction of this field has also been rapid development in recent years, which also makes the important branch of computer vision moving object tracking technology has more and more extensive application, such as intelligent security, intelligent transportation, safe driving And video analysis.
RETRACTED: Analysis of the Fine Management Strategy for the Cultivation of Vocational College Students’ Core Quality based on Big Data
Authors: Yanxia Wang , Qian Wang
Published: April 2021

The key to the development of vocational education is to improve the training quality of college students, and the key to improve the quality of college students is to cultivate the core quality of vocational college students. Based on the above background, the purpose of this paper is the application of big data in the fine management strategy analysis of Vocational College Students’ core literacy cultivation. This study adopts the methods of literature research, questionnaire survey and interview to study and investigate the current situation of vocational college students’ core literacy. This paper expounds the theory, practical significance and feasibility of the integration of data mining technology and fine management analysis in the era of big data from three aspects: the problems in the refined management analysis of traditional college students’ core literacy cultivation, the expansion and deepening of the fine management analysis by data mining technology, and the construction path of the fine management analysis platform. This paper puts forward the reflection and promotion strategies for the fine management of college students from three aspects: Deepening the concept of student management, internalizing the concept into the practice of student management, and constructing the mechanism of campus characteristic culture education. Finally, the study found that the core quality that college students lack most is social participation, accounting for 37.1%. Therefore, in order to change the current situation of college students training, the most important thing is to enhance the social participation of college students, so that college students can adapt to the needs of life-long development and social development.
Research on Automatic Processing of Worn Tread Test Data
Authors: Xiaojie Sun , Pengfei Gu , Cong Xu , Zhaoqin Li
Published: April 2021

Wheel tread wear monitoring is great significance to ensure the safety of train operation. In view of the large amount of data in daily test of tread and the error easily occurred in manual analysis and processing, a solution of automatic processing of tread wear test tool based on MATLAB is proposed. The automatic tool first loads the test data of WPA + wheel profile measuring instrument, eliminates the data with large error, gives the index of wheel wear and generates a report; then according to the generated report, it gives the analysis data of wheel wear, and draws the comparison diagram between the measured tread profile and the standard one, and finally gives the wear analysis conclusion. At last, some test data are used to verify the effectiveness of the program.
Design of English Autonomous Learning System Based on Digital Signal Processing
Authors: Kang Pingping
Published: April 2021

The network is beginning to change people’s production and life style, which has produced earth-shaking changes in many industries. The learning method based on the network also has a great impact on the traditional education. The emergence of individualized autonomous learning mode of college English with teachers as the leading factor and students as the main body makes students change from passive learning to active learning. Many college students take acquiring knowledge and information as their important purpose in the use of the Internet, which shows that online autonomous learning has become an important means of learning in universities. On this platform, students can flexibly grasp their own learning time and progress, so as to achieve targeted learning and further improve their interest in English learning. This paper expounds the construction of college English autonomous learning environment based on digital signal processing, and puts forward corresponding management measures in view of the existing problems in autonomous learning management, in order to give full play to the effectiveness of digital signal processing technology in assisting college English autonomous learning.
Research on Literature Aesthetic Education Based on Neural Network Algorithm
Authors: Yang Bofeng
Published: April 2021

Chinese traditional Chinese language and literature learning mode emphasizes the application of knowledge, and pays attention to strengthening students’ memory of literary knowledge and literary theory in the process of Chinese language and literature learning, but the cultivation of aesthetic education has not been implemented. With the great development of ideology and culture, literary workers have created a large number of literary works, which poses new challenges to the application of natural language processing technology. Chinese language and literature, as a treasure of the Chinese nation, contains rich aesthetic elements and plays an important role in improving people’s humanistic cultivation and cultivating people’s aesthetic consciousness. In the teaching of Chinese language and literature, aesthetic education is beneficial to highlight the core connotation of the major and promote the overall development of students. On the basis of expounding the value connotation of aesthetic education in Chinese language and literature teaching, this paper analyzes the application of aesthetic education in Chinese language and literature teaching, and explores the specific implementation path of aesthetic education in Chinese language and culture specialty based on neural network algorithm.
Research on Enterprise Financial Management Supervision Mode Based on Data Mining
Authors: Liu Bochuan
Published: April 2021

In the current era of rapid development of digital global economy, facing fierce market competition, managers’ decision-making behavior will directly affect the development of enterprises and determine the development direction. Under the background of big data era, it becomes more convenient and efficient for people to obtain data. However, people still face the confusion of complicated data and limited valuable information. People are eager to find a tool in order to realize effective processing of huge and complicated data in the shortest possible time. Through the application of data mining technology, it not only brings great convenience to the financial analysis of enterprises, but also can quickly obtain the required knowledge and information from a large number of data, providing important basis for enterprise decision makers. Data mining technology has many advantages, such as rapidity and accuracy, which opens a new development direction for enterprise financial analysis. This paper mainly discusses the application of mining technology in enterprise financial analysis in detail, which plays an important role in ensuring the competitiveness of enterprises in the market.
Construction and Development Countermeasures of Characteristic Towns Based on Geographical Big Data
Authors: Guomin Liu
Published: April 2021

This paper studies the hot spot distribution and Construction Development Countermeasures of characteristic small towns based on geographical large number. This paper first uses the big data method of Baidu map POI points of interest to explore the construction method of industrialization and urbanization transformation. Secondly, it analyzes the consumption transformation from the production-oriented economy in large and medium-sized cities to the multi pole enjoyment economy in urban and rural areas by using statistical big data, and points out that the transformation has the characteristics of market demand pulling and policy supply promoting. At the same time, this paper points out that the differentiation of location preference of four industries, namely, historical classics, traditional county economy, new economy and tourism, lays a foundation for the development of characteristic towns, but it also faces the dilemma of imbalance of urban system structure and failure of micro design. Finally, this paper proposes that it is necessary to complete the overall site selection and spatial scale intensity control with the help of big data, complete the regional spatial organization and the spatial supply of characteristic industries by means of data fusion, and organize and create the environment by micro characteristic spatial sequence. This conclusion has a certain reference value for the research on the hot spot distribution and construction development of characteristic towns.
Environmental Evaluation of Small Coastal Cities Based on Street View Images
Authors: Chen Jing , Wang Xiaoxuan , Wu Yujing , Shi Bing , Lu Jifu
Published: April 2021

Whether out of the need of theoretical exploration or practical application, carrying out urban environmental assessment research can attract the attention of the public and managers, and it is conducive to improving the existing environmental impact assessment system. With the continuous breakthrough of artificial intelligence technology and its application in various fields, it is possible to carry out urban environmental assessment research based on street view images on a large spatial scale. Based on the analysis of the theories and methods of urban environmental assessment, combined with the landscape vision application in the planning of small coastal cities (littoral), this paper constructs an overall understanding of urban environment, specifically the assessment of environmental quality.
Establishment of Parallel Text Corpus of Equipment Manufacturing Industry Based on Data Mining Technology
Authors: Liu Dongxia , Liu Jianguo , Zhang Xianghui , Chen Manqian
Published: April 2021

In the era of language big data, traditional data analysis methods can’t analyze semi-structured or unstructured data such as text, but all the contents in the equipment manufacturing corpus belong to text data. The equipment manufacturing corpus is a linguistic information base for legal activities and equipment manufacturing research, which aims to study equipment manufacturing and collect equipment manufacturing cases. At present, the construction of legal database in China is not perfect, and there are still many problems. In this paper, a method based on template transformation is proposed to automatically acquire parallel corpus on the Internet, and a method based on the number of transformation patterns and the retrieval and sorting of transformation patterns is adopted to verify bilingual parallel texts. This system can build a large-scale parallel corpus of equipment manufacturing industry by automatically acquiring a large number of parallel texts from the Internet.
Research on Translation Processing of Complex Long Sentences Based on Multi-strategy Analysis Algorithm
Authors: Liu Jing
Published: April 2021

Syntax analysis depends on a certain grammatical theory. In formal grammar system, dependency grammar has gradually become the focus of researchers. In this paper, an automatic translation method of English-Chinese terms based on multi-strategy fusion is proposed, which uses the term case base to optimize the translation of terms with low evaluation scores obtained from the translation quality estimation method. Through the levenshtein algorithm, we find a number of terms translation examples with high similarity with the source language of the terms to be optimized, including grammatical semantic features, sentence length, punctuation marks, functional words and context conditions, etc., to segment and simplify complex long sentences and compound generate translations. By designing the same knowledge representation for different languages, the algorithm can be universally used for translation systems of different languages.
Analysis of Factors Influencing Intercultural Communication Competence Based on Big Data
Authors: Rui Xun , Tong Sun
Published: April 2021

The cross cultural communication is an important part of cultural communication and language learning. Based on the method of big data analysis, 400 college students are divided into three groups to test their intercultural awareness and intercultural communicative competence. The test paper covers cross-cultural awareness, communication culture and knowledge culture. The results show that Chinese college students’ deep intercultural communication awareness is generally weak. Intercultural communication course can effectively improve the level of cross-cultural communication. The main mistake in cross-cultural communication lies in the negative transfer of culture. The results of this paper have a certain reference value for the analysis of the factors influencing intercultural communication competence under big data.
Ballistic Target Signal Separation Based on Differential Evolution Algorithm
Authors: Li Yuxi , Shi Baodai , Pan Xinrui
Published: April 2021

During the course of the ballistic target’s mid-flight, it is very important to accurately identify the target. Separating the target micro-Doppler curve is the key to accurate identification. Aiming at this problem, this paper proposes and improves a differential evolution algorithm to separate the micro-Doppler curves of the scattering points. According to the results of the simulation experiment, the signal is separated well, which verifies the effectiveness of the proposed algorithm.
NDN congestion control based on fuzzy comprehensive evaluation algorithm
Authors: Cui Gaolin , Wang Hui , Fan Zhihui , Li Peiyu
Published: April 2021

The congestion control algorithm of the named data network is not accurate in obtaining the congestion status, and the research is not carried out in time, and a congestion control strategy based on the fuzzy comprehensive evaluation algorithm is proposed. This strategy uses the fuzzy comprehensive evaluation algorithm to calculate and analyze the above two network parameters to obtain the network congestion state by detecting the buffer queue length of the network intermediate node and the actual and predicted round-trip time ratio. In order to refine the degree of network congestion, a variety of congestion is designed. The feedback signal uses the existing NACK feedback mechanism of the NDN network to send a congestion signal to the downstream node, and the downstream node adaptively adjusts the interest packet sending rate according to the congestion signal to achieve the purpose of reducing network congestion. In the NDN-SIM simulator, this algorithm is compared and analyzed with mainstream algorithms, and it is confirmed that this strategy can make full use of network resources and has higher transmission efficiency.
Discussion on Legal Model of Intellectual Property of Computer Software
Authors: Hou Kewen , Zhang Meili
Published: April 2021

With the development of computer software and information technology, software industry has become one of the important engines of modern economic construction and structural transformation in China. However, because the computer software is very complex and the industry value is high, it is easy to lead to the flood of intellectual property infringement, which is undoubtedly a great blow to the researchers in the field of computer technology and related industries. Therefore, through analyzing the current situation of intellectual property protection of computer software, this paper fully discusses the comprehensive construction mode of copyright law, patent law and trade secret protection law, in order to ensure the innovation and application environment of this intellectual property right.
Identification of depression using support vector machine with different connectivity
Authors: Yang Jiale , Li Liyuan , Shen Fei , Zeng Ling , Li Rong
Published: April 2021

Patients with depression have shown attention bias and inhibition deficits of negative emotional valance. However, it is not clear how the distributed brain networks support the function of inhibition and whether the modulation altered in depression. Thirty-seven patients with depression and 37 matched controls were undertook the audio-visual emotion task-fMRI and whole-brain psychophysiological interaction analysis was employed to obtain three different types of connectivity features, including task-modulated connectivity (TMC), task-independent connectivity (TMC) and task-functional connectivity (TFC). Support vector machine method was used to classify depression and explore the relation of reaction time prediction. Results indicated decreased modulation related to frontoparietal cortex, increased in temporal lobe and sensorimotor system in depression. Moreover, TMC performed better in predicting RT, while TIC and TFC had better classification performance. This study reveals that aberrant modulation of neural response is widely associated with the inhibitory dysfunction in depression and support that different connectivity features provide supplementary information for underpinning the functional integration and its alterations of brain networks.
Classification and Evaluation for Microblog Popularity Prediction
Authors: Ji Gang , Zhu Yuxuan , Niu Yukai , Hu Kai
Published: April 2021

In recent years, with the rapid development of the Internet, especially the mobile Internet, social networks have entered the stage of vigorous development and become one of the main sources of information. User-generated contents (UGC) on social platforms can spread information along social networks at an astonishing speed. Existing literature has proposed many prediction methods for the popularity prediction on social networks. This paper presents a classification and establishes a unified evaluation framework of popularity prediction methods for microblogs. More specifically, we divide these mainstream prediction methods into four types: feature based methods, time series methods, collaborative filtering methods and deep learning methods and conduct experiments on the real-world weibo data using these methods to predict. Finally, according to four indicators, including accuracy, efficiency, robustness and bias, we evaluate and compare the methods. Based on the prediction and evaluation results, this paper summarizes and draws the following research conclusions:(1) The deep learning method has the characteristics of high accuracy, high robustness and low bias. The DeepFM method, one of the deep learning methods, performs better than the other three prediction methods when using temporal data as its input. (2) The feature based methods only using temporal features are basically consistent with those using all available features, indicating that the temporal feature has strong prediction power. Therefore, the ‘peeking’ strategy that monitors the early response of users in the initial period after the items are posted is effective. Additionally, the predictive power of temporary features can be further amplified in time series methods and deep learning methods. (3) Due to the sparse user-item interaction in social networks, the accuracy and efficiency of collaborative filtering methods are low, which makes it impossible to predict the popularity of items in social networks well.
Research on high reliability in redundant link aggregation mode
Authors: Yin Xiaogui , Gan Weimin
Published: April 2021

Now, with the rapid development of the Internet, the requirement of bandwidth and reliability of backbone link is higher. In traditional Ethernet networks, it needs to pay a high price to increase the network bandwidth, if you use high-speed equipment, so not only the cost is high, but also the network is not flexible enough. In order to ensure the stability and reliability of Ethernet network, this paper proposes a redundant link aggregation technique, without replacing the equipment, and bundling multiple physical interfaces into a logical interface to increase the bandwidth of the link, dynamic and effective backup links can also be used to improve the reliability of links between devices. Redundant link aggregation can not only improve network bandwidth resources, but also solve the problem of network congestion.
Study on face detection based on an improved Gaussian skin color model
Authors: Tan Lingli
Published: April 2021

An improved Gaussian skin color model is proposed for face detection applications. It overcomes the issue that the traditional models become ineffective in extracting the skin color region in the presence of diverse skin colors and illuminations. It is a two-dimensional gaussian mixture model based on H-SV (i.e., an improved HSV) and YCbCr color spaces. Experimental results from several face image databases show that the detection accuracy is improved by 35.8% and 6.3%, respectively, compared to a traditional gaussian skin model and Adaboost algorithm.
Adaptive algorithm for feature selection of speech emotion recognition based on genetic algorithm and SVM
Authors: Chen Hong , Zhang Bo
Published: April 2021

As a key carrier for people to obtain information in daily life, speech not only provides text signals, but also contains emotional signals. As an important factor of semantics, emotion signals are the focus of improving the intelligent level of human-computer interaction. Therefore, it is important to improving the accuracy of speech emotion recognition. This paper proposes to incorporate derivative feature parameters into the voice emotion recognition feature selection, and to improve the feature selection algorithm based on genetic algorithm and SVM. This article may provide technical support to promote the development of artificial intelligence.
TDA 3D Data Model and Feature Simplification Analysis
Authors: Li Ou
Published: April 2021

Topology data analysis (TDA) is a combination of topology and data science. The analysis process will not cause information loss. It has been widely used in various industries in recent years. However, how to display TDA data with simpler data structure is still under exploration. This paper designs and simulates a method of TDA point cloud data segmentation and classification data topology information simplification. Firstly, the point cloud data is segmented based on the region growing method. Secondly, the idea of “topological compression” is used to express the richer data information of each segmentation part with a simpler data structure; in the experimental model, the experiment is completed on the platform of MATLAB 2016 (b), and the simple and complex representation of segmented vehicle targets completely includes all the pixel positions and gray 3D data information of the vehicle.
Research on NSGA-II Flexible Job Shop Scheduling Based on Variable Length Chromosomes
Authors: Hao Yuanfeng , Ji Chunlei , Xiao Wei
Published: April 2021

As the core of enterprise production management, production scheduling plays a pivotal role in the optimal allocation of resources and scientific operation. A more optimized workpiece scheduling method can greatly reduce the staying time of workpieces in the workshop and improve production efficiency. Most of the existing scheduling algorithms are coded according to the length of the number of workpieces and machines, and the number of workpieces in each batch of the factory is often different. This scheduling method has certain limitations. How to perform different batches and varying numbers of workpieces? Production scheduling has become an urgent problem for modern enterprises. Based on this, this paper proposes a variable-length chromosome encoding method, which can perform production scheduling on different batches and different numbers of workpieces, greatly improving the scope of application of scheduling scenarios. At the same time, because of the shortcomings of low population diversity and slow operation speed in the non-dominated sorting genetic algorithm, an adaptive layering strategy is proposed. Simulation experiments show that the improved algorithm can better complete scheduling tasks and improve production efficiency.
The Research on Single Page Application Front-end development Based on Vue
Authors: Li Nian , Zhang Bo
Published: April 2021

‘Vuejs’ is a popular front-end framework, which uses MVVM (Model View View-Model) design pattern to support data-driven and component-based development. This paper discusses the basic working principle and development mode of Vue, and on this basis, uses SPA (Single Page Application) to design and implement a mobile mall front-end page based on Vue. This research fully demonstrates the advantages of front-end and back-end separation technology in Web development, and provides a powerful front-end support for full stack development.
Data assimilation model based on machine learning
Authors: Lang Jialin , Qiu Feng , Wu Pin
Published: April 2021

Data assimilation(DA) is a method mainly absorbs the observation data into the simulation model, integrates the errors of observation and simulation, and provides a more accurate state so as to reduce the forecast error. Data assimilation has been widely used in the fields of atmosphere and ocean. However, traditional data assimilation methods require a lot of computing resources and consume a long time. Machine learning is a data analysis method with strong learning ability and rapid prediction ability. Long Short-Term Memory network (LSTM) is a widely used machine learning model, which has good effect on time series prediction. In this paper, we use the historical data of data assimilation to train LSTM model and get the prediction model. The experimental results show that the LSTM model can learn the latent law from the historical data, the results of the model fit the real data well, and the calculation speed is greatly improved compared with the original data assimilation algorithm.
A Time-Based Online Customer Behaviors Analysis Model
Authors: Qin Zeqing , Wang Zesong
Published: April 2021

With the advent of the big data era, reviews are common when shopping online. To better understand the connection between reviews and the quality of the real product, we have to use mathematical language to simplify the problem. This relationship can help both the buyer to get what they want and the firms to make better products with attractive features. To help us understand how the indicators like star ratings can reflect the product’s popularity, we construct a series of models to illustrate them. We first use the NLP method to process text type data, such as product review information, and convert these text data into sentiment scores, with the help of the k-means++ algorithm, we cluster the data into 3 categories. These categories can help the company to track the performance of their products by simply calculating which category the product belongs to. Next, a Time-based Reputation Judgment Model combined with AHP and Markov Chain Model was established to describe the change of reputation over time. In this way, a company can easily predict the future trend of their merchandise sales.
Prediction of Corrosion Resistance of B10 Copper-Nickel Alloy Based on Optimal Convolutional Neural Network
Authors: Dong Yuehua , Hu Donghai , Wang Zhigang
Published: April 2021

Aiming at the defects of insufficient number of corrosion resistance prediction models and simple feature extraction of B10 copper-nickel alloy, a corrosion resistance prediction model of B10 copper-nickel alloy based on optimized convolutional neural network is proposed. The convolutional neural network architecture for grain boundary image characteristics is proposed to conclude a step-by-step convolution operation by analysing the traditional convolution operation process, and it proves theoretically that such a step-by-step convolution operation can reduce the consumption of additional parameters; learning pool proposed single-channel operation can reduce the loss of feature information; the multi-layer feature fusion learning strategy makes the expressive force of deep network extraction features diversified. The results of multiple experiments demonstrate that the improved algorithm introduced can improve the prediction accuracy of the model in image classification comparing with the traditional convolutional neural network model, and the improved convolutional neural network model can better achieve the prediction of corrosion resistance of B10 copper-nickel alloy.
Research on Logistics Distribution Path Planning Based on Fish Swarm Algorithm
Authors: Xiong Chuanyu , Xu Youhua
Published: April 2021

Logistics distribution related to the transportation cost and the competitiveness of logistics enterprises. How to effectively determine the shortest path of logistics distribution has become a key link for logistics enterprises to reduce costs, improve efficiency and enhance competitiveness. This paper proposes a method of using Fish Swarm Algorithm to optimize the logistics distribution path. Experiments show that the method is effective. The algorithm can quickly jump out of the local optimal solution and converge on the shortest path. The optimized path length is less than half of the initial length.
Design and Implementation of Occlusion Image Recognition Algorithm Based on Deep Convolution Generative Adversarial Network
Authors: Liu Zhijun , Cai Feng , Pan Xuefeng
Published: April 2021

In the image recognition process, the accuracy of image recognition is affected due to partial coverage or light problems. This paper proposes a method to solve the problem of target occlusion based on a deep convolution generation confrontation network. The method actively occludes the feature map after the feature is extracted to generate the confrontation sample, and generates a map and a mask from the input image. At the same time, a method is proposed. This new loss function applies the algorithm to handwriting recognition and trains the network through large-scale sample data sets. Experiments show that this method significantly improves the accuracy of image extraction.
Research and implementation of convolution k-means algorithm based on unsupervised learning
Authors: Suyu Huang
Published: April 2021

This paper proposes a framework combining the advantages of unsupervised learning, convolutional neural network and K-means clustering algorithm. When there are few label data available, convolution k-means algorithm is formed by combining k-means algorithm with convolution neural network. Through the learning of convolutional neural network and clustering analysis, as well as the continuous optimization and adjustment of the network, the convolutional k-means algorithm is used to train the deep convolutional network, and the hierarchical function of unsupervised learning technology is used to reduce the dependence on a large number of labeled data. Through the analysis of the experimental results, compared with the supervised learning convolution k-means algorithm, the unsupervised learning convolution k-means algorithm can better represent the data clustering with the increase of the number of filters, and can improve the accuracy of test classification. The unsupervised learning convolution k-means algorithm is better than other unsupervised learning filter methods.
Analysis on Development Tendency of Computer Vision and Graphics based on Bibliometrics
Authors: Ci Yutong , Jiao Fangyu , Tu Wenyan , Fang Ji
Published: April 2021

In this paper, the bibliography included in Web of Science is taken as the data sample. This paper analyzes articles and reviews published from 2010 to 2020 in the field of computer vision and graphics to reveal the major countries or regions. We use leiden-type community algorithm to identify research topics. Besides, we explore the research fronts in the field of computer vision and graphics based on highly cited papers. The results show that the research on computer vision and graphics in the United States started earlier. The United States has always been in the leading position. China has developed rapidly in recent years. The United Kingdom, South Korea and India are also prominent. Deep learning, face recognition, image retrieval and volume rendering are the hottest research topics in the field of computer vision and graphics. Convolutional neural network and deep learning are the most emerging research fronts in the field of computer vision and graphics.
Design and Implementation of Crop Automatic Diagnosis and Treatment System Based on Internet of Things
Authors: Pan Aimin , Wang Nan
Published: April 2021

This paper designs a smart agriculture Internet of Things system which combines crop disease detection with automatic diagnosis and treatment, and provides support for subsequent research on crop disease detection and control. The smart agricultural Internet of Things system uses TCS3200 colour sensor to identify leaf disease characteristics; colour sensor sends the data back; This system compares it with the recorded plant identification database, and carries out small-scale sprinkler irrigation diagnosis and treatment through valve irrigation system. The Internet of Things system consisting of STM32L431 single chip microcomputer, MG811 CO2sensor, BH1750 illumination sensor and DHT11 temperature and humidity sensor monitors environmental data, which is uploaded to the cloud by BC35 module through NB-IoT protocol and can be viewed by users on the display screen. Users can adjust the exhaust system, lighting system and humidification system in the system through the cloud or mobile phone to improve the environment and alleviate crop diseases. The experimental results show that the intelligent agricultural Internet of Things system designed in this paper has achieved the initial goal, and can roughly judge the disease types of plants by leaf colour, which has great commercial application value.
Research on the Technology Realization of Network Mass Information Processing Platform
Authors: Zhao Qiang , Liu Sha
Published: April 2021

In recent years, With the rapid spread and development of the Internet, especially the huge reserve of information and convenient search function, it attracts more and more of the community to the Internet users ranks. But these need supports of high-performance computer technology. Therefore, in order to enhance the capabilities of Internet vast amounts of information intelligent parallel processing, and promote the comprehensive development of the overall system of information storage, management, integration and intelligent processing, data mining, information retrieval and utilization functions, I proposed a massive information processing platform based on high-performance data mining. The one hand, it analyzes the key technologies to achieve the platform; the other hand, it gives a brief introduction to its hotspot applications. Therefore, it provides a reference and guidance with real-world applications for the technology of high performance and highly efficient network of mass information processing platform.
Clustering analysis method of power grid company based on K-means
Authors: Wang Wenting , Ma Qiang , Liu Yong , Yao Ning , Liu Jing , Wang Zhaoxuan , Li HongLin
Published: April 2021

With the development of network and the expansion of power grid business, the power grid traffic becomes more and more complex and huge. It is more and more difficult to identify the power grid business through the traffic, and the identification accuracy also needs to be improved. In order to solve this problem, this paper proposes a clustering scheme based on K-means to classify the power grid business through the grid traffic, using elkan K-means algorithm improves the efficiency of the algorithm and finds the most suitable K value for power grid business data.
Test Case Generation Method based on Generative Adversarial Network
Authors: Nie Jiaojiao , Fan Xianglin , Wu Yushuang
Published: April 2021

Traditional fuzzing tools generate low diversity of test cases and low vulnerability detection efficiency. This paper uses a test case generation model based on a generative confrontation network. The model uses LSTM as the generation network to generate data. The model uses fully connected network to construct a discriminant network for classification, and the test cases with the same protocol format are automatically generated after training. Finally the model uses the generated test cases to attack the protocol system to detect vulnerabilities. This method is compared with the traditional tools AFL, Peach and Sulley on the Modbus protocol, and the validity of the method is verified.
A method of terminology alignment in Historical Books based on the correspondence between Pivot language and position
Authors: Xie Xingchen , Wu Xiaoting , Wei Xiaopeng , Che Chao
Published: April 2021

In view of the most difficult problem of terminology translation in the translation of historical books, this paper extracts term translation pairs from bilingual parallel corpus as a reference to improve the quality and efficiency of the translation of historical books. However, the small size of the corpus of historical books leads to poor word alignment, which brings a large number of errors to term alignment. At the same time, the special grammar of ancient Chinese will also affect term alignment. In order to solve the above two problems, this paper proposes a method of term alignment of historical books based on pivot language and position correspondence. This method introduces modern Chinese as the pivot language, makes use of the position correspondence between modern Chinese and English terms in sentences, and uses the sliding window method to generate candidate term pairs, which avoids the influence of word alignment errors on term alignment. In addition, if we first align to modern Chinese, the ancient Chinese terminology will complete the omitted characters in modern Chinese and replace the false characters, so as to avoid the influence of the special usage in ancient Chinese on the term alignment. The experimental results in Historical Records show that the accuracy of this method is slightly better than that of the method based on word alignment, which verifies the effectiveness of this method without word alignment.
Infrared Small Target Detection Based on Multi-Scale Local Contrast Network
Authors: Liu Yuanteng , Wang Yuehuan
Published: April 2021

To mitigate the challenge of the lack of the intrinsic features of the small target which possesses only few pixels, we designed a multi-scale local contrast network which combines features extracted from different scales and layers for single-frame infrared small target detection in this paper. We extract the multi-scale contrasts features in the same layer by multi-scale local contrast module and fuse the cross-layer contrast features by local contrast fusion module. Moreover, we embed these modules at each stage as a depth-wise nonlinear layer into an end-to-end convolutional network to predict the small target. We conduct extensive experiments and comparison with other SOTA methods to verify the performance of our method. The results show that our method outperforms many of its competitors, and when taking computation cost into consideration, it will be more outstanding.
Classification Problem Using Imbalanced Ratio Based Random Forest Method
Authors: Wang Wan , Guo Junlin , Chan Wai Kin Victor
Published: April 2021

In recent decades, as machine learning is getting more and more popular in many fields, multiple classification methods have been proposed and applied to various applications for intelligent decision making. Most of the classification methods are just work on a shallow machine learning classifier. And many of these algorithms are already existed in the machine learning package. In this study, a new Imbalanced Ratio Based Random Forest(IR-RF) method was proposed to make classification and prediction. We focused on dealing with the imbalanced data. So we use several imbalanced datasets and put processed features into our IR-RF model. Three datasets we used are from the UCI machine learning database where RF and IR-RF are compared in the final results. The former RF method indicates the traditional random forest algorithm and the latter IR-RF method is our newly written one. Comparing to other classification model, IR-RF is an algorithm-level method, which is not limited by existing parameters. We can generate more new parameters within the algorithm and train our own classifier. Results show that proposed method has higher accuracy than traditional random forest method after our ratio compared and it can take better account of imbalanced condition. So proposed method not only performs well in high accuracy but also can be applied to especially imbalanced condition which is a new window for practice.
Research on Post-Quantum Cryptosystem Based on IoT Devices
Authors: Wang Song
Published: April 2021

With the emergence of quantum computers, public-key cryptographic algorithms commonly used in classical computers are no longer secure. However, the current post-quantum cryptographic algorithms are mainly concentrated on the desktop CPU of Intel and AMD. For the Internet of Things, which is increasingly widely used, the research on post-quantum cryptographic algorithms is still scarce. Based on the research of post-quantum cryptosystem, this paper explores the post-quantum key algorithm based on Internet of Things devices.
Design and optimization of two-step verification scheme for form data
Authors: Aihua Wang
Published: April 2021

In the era of big data, we submit a lot of data to the server through the web form interface every day. Data validation is an indispensable link in the process of submitting data. The current verification scheme has some problems, such as untimely verification data, heavy load on the server. This paper analyzes the defects in the single step method in the form data verification by case analysis. On this basis, the two-step scheme of data verification is proposed, the design and optimization ideas of the two-step scheme are given, and the process and principle of the two-step verification are described. The two-step method of form data validation provides an available solution for efficient and fast data validation.