bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on combat simulation system based on Multi-Agent
Authors: Wang Jianwei , Qiu Shaoming , Liang Ting , Du Xiuli
Published: January 2021

Combat simulation is an important part of simulation war. There is a big error between the analysis, processing and calculation of the battlefield situation of the combat simulation system developed by various scientific theoretical methods of the traditional linear machinery and the actual situation. Firstly, the method of combat modeling and simulation based on Multi-Agent and the process of combat model modeling based on multi-agent are studied, and the combat scenario is formulated. Secondly, the types of agents are extended, and the roles and relations of each agent are emphasized. Finally, based on the anylogic development platform, a new complex multi-agent combat simulation model is proposed, which expands the types of agents in the combat simulation model. The simulation results show that the agents in the new complex multi-agent combat simulation model are more comprehensive and more close to the real combat scene of modern war.
Research on the Application of Vocabulary Relation Extractio n Method of Demand Entity Based on Bi-GRU
Authors: Zhao Jiakun
Published: January 2021

Designing and mining the entity vocabulary relationship of requirements engineering to form a knowledge base of requirements engineering has a positive effect on providing the intelligent service of demand and software development in the future. But there are very few targeted extractions of the relationship between demand vocabulary in the current demand field. Therefore, this paper proposes a demand entity vocabulary relationship extraction model consists of the Bi-GRU, the attention mechanism and the labeling and filtering layer of the demand entity. This paper discribes how to use the data from actual projects to verify the methods above. The experimental results show that the method of relationship extraction combining the attention mechanism and the labeling and filtering layer of the demand entity in the Bi-GRU neural network has a significant effect in the demand vocabulary relationship extraction.
Air quality prediction method based on improved wavelet denoising and LSTM
Authors: Pu Teng , Lin Fang , Zhao Yuting , Fu Zhenxiang
Published: January 2021

in order to optimize the traditional wavelet de-noising algorithm threshold function in continuity and noise reduction effect, an improved soft hard threshold compromise method and deep learning model are proposed to analyze the prediction performance of different deep learning models and improved wavelet algorithm. RNN, GRU and LSTM neural networks are constructed to predict the original data, the traditional wavelet denoising data and the improved wavelet denoising data. The air quality data of Chengdu were used for simulation experiment. The results of quantitative analysis showed that: for AQI, PM2.5 and O3_ Oh denoising will cause the loss of useful signals. The improved wavelet threshold denoising for PM10, SO2, Co, NO2 data can improve the prediction effect of the model. The results show that LSTM model has good applicability in air quality prediction, the absolute percent error is 5.867%, and the mean square error is 4.870. The prediction performance of LSTM model is about 10%, the absolute percent error is 5.176%, and the mean square error is 5.314. The traditional threshold denoising will affect the prediction results of the model for the actual data due to the loss of high-frequency signal and pseudo Gibbs phenomenon. The results show that the improved soft hard threshold compromise method can better deal with air quality data, and the LSTM model based on improved wavelet denoising has strong applicability for air quality prediction.
RETRACTED: The construction of evaluation system for the cultivation of statistics professionals under the background of big data and artificial intelligence
Authors: Tian Xiujie , He Dongxia , Lu Chunyan
Published: January 2021

The high development of big data and artificial intelligence has put forward higher requirements for statistical talents, and the quality of statistical talents needs to be further improved. Based on the present situation of statistics teaching in our country, this paper takes the cross characteristics of big data and artificial intelligence as the research background, and aims to break through the evaluation model that regards the evaluation of teachers teaching work as the only evaluation content, to promote the four substantial transformations of the subject of judgment, the objective of judgment, the content of judgment and the object of judgment, meanwhile, to set up the corresponding index system.
Vehicle detection algorithm based on LW-SSD
Authors: Liu Yunxiang , Zhang Guoqing
Published: January 2021

Due to the complex structure of the traditional SSD network model and the large amount of parameters, it is not suitable for porting to mobile or embedded platforms, so the LW-SSD vehicle detection algorithm is proposed, this paper redesigns the basic feature extraction network of SSD, using MobileNet feature extraction network instead of VGG16, for the original MobileNet to improve, remove the original MobileNet network average pooling layer and Softmax layer, while reducing the MobileNet network from the original 28 layers to 13 layers. Then, on the basis of the subsequent network, feature maps of different scales are scaled, residual mapping and deep feature fusion operations, and the low-level position information and high-level semantic information are effectively combined to obtain new feature maps of different scales. The designed network model is trained using the Tensorflow framework, and the test effect of the network model is evaluated. The experimental results show that the accuracy of the network model designed in this paper is 78.76%, which is 9.27% lower than the original SSD network model. However, the amount of parameters is only 1/4 of the SSD network model. At the expense of partial accuracy, the design of a lightweight network model is exchanged, which lays the foundation for subsequent development on mobile or embedded platforms.
Research and application of data mining algorithm
Authors: Huang Cong , Yang Yao , Wang Huajun , Zhang Xueyu , Zhao Jinquan , Wan Jun
Published: January 2021

With the breakthrough development of a series of technologies such as machine learning and internet of things, data plays an increasingly important role in everyone’s daily affairs and work. With the explosive exponential growth of data volume, data types are also quietly changing, from the traditional single, structured data to today’s diverse, semi-structured data. In the face of emerging new massive data, data mining technology has gradually become the focus of attention. Data mining was also known as knowledge discovery (KDD) at that time. It was mainly defined as a pattern hidden in massive data, which must be understood by people and bring potential benefits. In this paper, we mainly studies the basic principle and algorithm knowledge of data mining, and applies ridge regression and random forest algorithm model in real estate price forecasting. Finally, through the stacking thinking of ensemble learning in ensemble learning, we propose a fusion method of ridge regression and random forest model, and obtain a more accurate and stable prediction model.
Effectiveness of handshake strategy in 3D underwater acoustic networks
Authors: Zhao Danfeng , Lun Guiyang
Published: January 2021

Underwater acoustic networks (UWANs) have emerged as a promising technology to accomplish Internet of Underwater Thing (IoUT). Handshake-based protocols have been used in UWANs to resolve various problems for different protocol layers. However, the effectiveness of handshake is seriously influenced by interference as the underwater channel has lower spreading loss factor and absorption attenuation. In this paper, we derive effectiveness of handshake strategy for 3D UWAN and analyse it with various parameters.
Text sentiment analysis based on Glove model and United Network
Authors: Li Duojiao , He Chenwan , Chen Ming
Published: January 2021

Traditional convolutional neural network has been proved to have good classification effect in text sentiment analysis experiments. However, CNN ignores the context dependent information of sentences, which affects the classification effect. Therefore, this paper proposes a text sentiment analysis model based on CapsuleNets and Attention mechanism. Firstly, the word vector is trained by using Glove model; secondly, the local features are extracted by using the features of double-layer capsule network; then, attention mechanism is added to assign the corresponding weight to the extracted text information; finally, we complete classification. The experimental results show that the proposed model can effectively improve the accuracy of emotion classification.
Research on Target Tracking Algorithm in occlusion scene
Authors: Zhao Jianguang , Cao Xinying
Published: January 2021

Target tracking is widely used in video image, intelligent transportation, behavior analysis and other fields. At the same time, target tracking technology faces more and more challenges. In this paper, the target tracking technology in occlusion scene is studied, and the target tracking method based on spatiotemporal context and the target tracking method combined with depth network model are introduced respectively. The main ideas and principles of the two methods are analyzed, and the improvements based on these two methods are summarized. Finally, the problems existing in the future development of occluded target tracking algorithm are prospected.
Research on syntactic dependency tree and Ontology constraint in remote Supervising relation extraction
Authors: Zhao Shuxu , Zhang Yiguang , Wang Xiaolong
Published: January 2021

At present, research on remote-supervised relationship extraction fail to fully consider the position information among words in a sentence, and the extracted relationship may be inconsistent with the actual relationship. Therefore, this paper combines syntactic dependency tree and ontological constraint to carry out remote-supervised relationship extraction. The syntactic dependency tree is used to obtain the position weight of each word in the sentence and the domain ontology constraint is introduced to improve the accuracy of the extraction relationship. Experiments on Freebase+NYT data set show that this model can effecttively reduce the noise interference of wrong labels, and the model improves the accuracy by 2% compared with other reference models, thus better realizing relation extraction and laying a relevant foundation for the construction of high-precision knowledge map.
Sentiment analysis based on BiGRU information enhancement
Authors: Yin Xing , Liu Changhui , Fang Xiaodong
Published: January 2021

In view of the existing sentiment analysis technology, model design is often a combination of different network models, which may not give full play to the advantages of the network. This paper proposes a data pre-training based on BERT, and input the obtained data into BiGRU neural network layer to enhance the features, and achieve the feature enhancement by stacking and repeated use. The input of the second layer is the result of the output of the first layer, and the input of the third layer is the result of the output of the second layer, and then it is calculated multiple times in different subspaces to learn relevant information. The experiment was conducted on the Twitter data set, and the results showed that in this idea, The two-layer BiGRU classification effect is better, reaching an accuracy of 82.63%, the convergence rate is faster, and it is better than other classification models, and the classification work can be completed well.
Research on Network Data Security Based on RS-PS Support Vector Machine (SVM)
Authors: Su Li , Yu Linlin , Li Ting , Liu Xinxin
Published: January 2021

In the era of rapid development of digital information, resource sharing under the Internet environment, convenient access to information has brought us great convenience. However, as a new independent variable, the Internet itself has many unknown vulnerabilities, which provide an attack entrance for attackers and cause serious security risks. In this paper, intrusion detection research based on rough vector set and particle swarm optimization mainly involves particle swarm optimization (PSO), learning of rough function, etc. Based on some shortcomings of the algorithm optimization in the field of intrusion detection, a rough set(RS)-particle swarm(PS) SVM network data security detection method is proposed to provide security guarantee for information services.
Research on Scientific Research Data Platform Based on Big Data
Authors: Yuxiang Ji
Published: January 2021

In order to realize the fault tolerance of real-time tasks in scientific research data platform information platform, based on adaptive feedback equalization and symbol modulation technology, a fault-tolerant scheduling model of real-time tasks in scientific research data platform information is proposed in the paper. What is more, the platform transmission channel model of the scientific research data platform information under routing conflicts is first constructed to optimize the information transmission protocol of the scientific research data platform. Secondly, the fuzzy C-means clustering method is applied to perform the inspection robot information fusion. Meanwhile, the symbol modulation method is used for fault-tolerant scheduling of real-time tasks in the information platform. Finally, a simulation experiment is carried out. The results show that the method proposed in the paper has better fault tolerance for platform real-time task scheduling of scientific research data platform information, and the channel balance of the information platform is stronger, which improves the platform real-time task scheduling capability of scientific research data platform information.
Research on Optimization of Big Data Fusion QM Model Based on Block-chain Distribution
Authors: Zeng Weijia , Qin Fang , Guan Sheng , Jiang Jing Jing
Published: January 2021

In terms of poor classification and detection effect of big data fusion QM model as well as weak feature mining ability, a big data fusion QM model based on block-chain distribution optimization is proposed in the paper. Moreover, a block storage structure model for the distribution of big data is first established on the block-chain to perform shared scheduling according to the attribute distribution of the distributed big data on the block-chain, and then the big data of the distributed big data on the block-chain is mined. On this basis, the differentiated feature distributed retrieval method is used to cluster the distributed big data information on the block-chain, and the rough set feature quantity is extracted from the fuzzy cluster center. Meanwhile, combining the fuzzy correlation feature detection method, the information of the block-chain distributed big data is reorganized to extract the statistical features of each module. In addition, the quantitative recursion in the process of block-chain distributed big data fusion is analyzed, so that the optimization design of the QM model of block-chain distributed big data fusion can be realized. The simulation results show that the feature resolution ability of block-chain distributed big data fusion with the method proposed in the paper is better, and the classification detection and feature mining capabilities of block-chain distributed big data are improved, which has high application value in the block-chain distributed big data information retrieval and scheduling.
A method to remove image noise based on 6-tap Filter Interpolation
Authors: Wang Nan , Zhang Ya-Di
Published: January 2021

A method to remove the noise of image based on 6-tap filter interpolation is investigated in this work. To this goal, the algorithm principle of 6-tap filter interpolation including 1 / 2 pixel interpolation process, 1 / 4 pixel interpolation process and 1 / 8 pixel interpolation process are firstly analyzed. Then, in order to validate the effectiveness of 6-tap filter interpolation for the image denoising, two images added with Gaussian noise are processed by 6-tap filter interpolation and the SNR is used to evaluate the post-processing image. The obtained results show the SNR of post-image is larger than that of pre-processing image, which proves that the 6-tap filter interpolation can be acted as an effective method to remove the noise of image.
Tradeoff analysis of SWIPT-Based regenerative relay communication system
Authors: Shi Xiaoye , Sun Jin , Zhang Zhaowei
Published: January 2021

It has been brought to attention that the energy-carrying relay system can not only increase the energy harvesting of the system but also reduce the energy loss of the system at the same time. In this paper, a trade-off metric is incorporated in the power allocation of the energy-carrying relay system, which can substantially meet the energy harvesting and data transmission requirements of various application scenarios in a more flexible manner. Subsequently, a trade-off metric is proposed, and an optimization problem for maximizing the trade-off metric value is constructed. Eventually, the optimal solution for optimizing the trade-off metric is obtained by applying Lagrangian multiplier method. It has been shown in experimental simulations that the trade-off metric has the minimum value of 0.4073 when ω=0.54, which represents the intersection of the three curves. It implies that the proposed trade-off metric can precisely describe the relationship between the probability of successful information transmission and the energy harvesting power. Most importantly, the trade-off metric proposed in this paper can well illustrate the relationship between energy harvesting and information transmission. Whether it is a symmetrical channel or an asymmetrical channel, where the optimal solution derived through theoretical calculations is consistent with the experimental result.
Design of intelligent distribution network based on Geographic Information Technology
Authors: Zhang Linyao , Hu Zhenda , Fang Xiaojie , Xu Ye , Cai Xiuwen , Chen Shu
Published: January 2021

According to the business requirements of distribution network planning, this paper deeply studies the existing simplified display algorithm of distribution network in GIS, constructs the distribution network model based on geographic information technology, develops the functions of supporting storage, display and display, and the function module of professional map editing for distribution network planning. The concept of spatiotemporal data and its application in GIS are innovatively put forward. The display research based on the status quo and the analysis and management technology of distribution network structure under planning state are carried out. The spatiotemporal representation, spatiotemporal evolution, and comparison of power grid are realized. The geographic image elements are matched and associated with equipment parameters to realize the integrated modeling of map and power grid, realize the penetration function from the map to equipment parameters and power grid indicators, and support the diagnosis and analysis of power grid-related indicators. Finally, a three-dimensional power map with time dimension, space dimension, and attribute dimension is constructed to provide geographic environment and equipment parameter data for power grid planning, site selection, and line selection.
Research on Spatial Positioning System of Fruits to be Picked in Field Based on Binocular Vision and SSD Model
Authors: Zhang Xing , Gao Qiaoming , Pan Dong , Cao Peng Cheng , Huang Dong Hui
Published: January 2021

The accurate fruit recognition in the field was one of the key technologies of fruit picking agricultural robots. An improved Single Shot Multi-Box Detector (SSD) model based on the color and morphological characteristics of fruit was proposed in this paper when aimed at the large collection workload and low secondary transfer efficiency of fruit such as palm fruit, durian, pineapple and other fruits grown in a complex field environment. A binocular depth camera RealSense D435i was used to collect images of the fruit to be picked in the field. Meanwhile, the MobileNet was replaced with the VGG16 basic network based on the Tensor-flow deep learning framework to reduce the amount of convolution operations for extracting image features in the SSD model, and a spatial positioning system for pineapple fruit was designed. Furtherly, experiments showed that the improved SSD depth detection model had a smaller size and it was more convenient to be deployed on the mobile end of agricultural robots, which the model had a high accuracy in the effective recognition of the fruits to be picked under the weed occlusion and overlapping scenes. The frame rate of the video reading and detection for the binocular depth camera reached 16.74 Frames Per Second (FPS), which had good robustness and real-time, and a good solution for the automatic picking of agricultural picking robots could be provided in the field.
A Face Detection Method Based on Skin Color Features and AdaBoost Algorithm
Authors: Yan He , Liu Yuhan , Wang Xiaotang , Li Mengxue , Li Huan
Published: January 2021

The face detection based on skin color features is greatly affected by illumination and the face detection based on AdaBoost algorithm is greatly affected by multi-pose of face, and this means that the large face deflection will make the detection less accurate. So we investigated a face detection method that combines the skin color model and the improved AdaBoost algorithm in this paper. This method first judges whether the collected face image needs illumination compensation. The image is converted from RGB color space to YCbCr color space with higher pixel clustering characteristics and distribution rules by whether it is needed or not. Meanwhile, the candidate regions containing face images are carried out through morphological process and shape select after establishing the Gaussian model. Finally, the face detection results are obtained through detecting the candidate regions with face images by improved AdaBoost algorithm. A large number of experimental results show that this new method can effectively reduce the false detection rate and improve the efficiency of face detection.
Research on Recognition Method of Car Fuel Tank Cap Based on Residual Network
Authors: Zhang Qingpeng , Li Yongqing
Published: January 2021

Aiming at the problem that traditional algorithms have low recognition accuracy for different car fuel tank caps, a car fuel tank cap recognition algorithm based on improved Faster-RCNN is proposed. Firstly, the VGG16 network in the feature extraction network link is improved to the ResNet-101 residual network to improve the recognition accuracy and system speed. Secondly, the ROI pooling of the original pooling layer is changed to ROI align regional feature aggregation method makes the target rectangular frame more accurate. Finally, the fully connected network is used to perform the classification of the car fuel tank cap and the accurate regression of the frame. Through the detection of 200 actual images of car fuel tank caps, the experimental results show that the algorithm can achieve 98.0% accuracy and a detection speed of 0.139 seconds per image. Compared with traditional image algorithms, it can obtain faster speed and higher accuracy, and can provide technical support for unmanned gas stations, which has certain engineering application value.
Design and Implementation of Lossless Compression System for CCSDS Hyperspectral Images
Authors: Zhang Lili , Liu Tao , Zhang Kechun
Published: January 2021

Based on the xc7k325tffg900 FPGA chip of Xilinx Company and combining the actual hyperspectral image compression task requirements, the calculating speed and compression efficiency of the algorithm module are optimized. In the design of the prediction module in this article, the two-clock solution is used to solve the problem that the algorithm speed is restricted due to the feedback of the weight update. And the RTL-level design is implemented on the FPGA. CCSDS 123.0-B-1 hyperspectral image lossless compression algorithm is achieved. The system uses block compression to deal with the data source to avoid the entire system compression data errors caused by a certain pixel compression error. So the system’s Error Resilience ability is improved. Finally the compressed code stream is decoded. The decompressed image is consistent with the raw one, which verifies the correctness of this design.
Micro-mechanism of low-density lipoproteins (LDL) transport in multi-layered arterial wall
Authors: Wang Erhui , Zhang Xuelan , Suleiman Kheder , Shu Chang , Zheng Liancun
Published: January 2021

This paper investigates micro-mechanism of LDL transport numerically in a multi-layer arterial wall. A novel comprehensive 3D model is developed by considering the coupling interactions between vascular layers, and numerical simulations are carried out for a two-layer structure arterial wall. Results displayed the effects of dynamic viscosity on LDL transport. Moreover, we present data demonstrating that endothelial cells have a great blocking effect on the permeation and transmission of macromolecules by the different vascular wall. Furthermore, to verify the reliability and efficiency, a comparison is presented which shows that our numerical results are in good consistency with the experimental data in the literature.
Design and Implementation of Waste Reduction Intelligent Cafeteria System Based on Raspberry Pi
Authors: Zhang Shuai , Wu Qiwen , Zhu Tianyin , Hou Keyu
Published: January 2021

With the development of higher education in China, the number of college students on campus is increasing, and the number of people eating in college cafeterias also increases. On the one hand, the problem of crowded lines in school cafeterias during peak dining periods is becoming more and more serious, and students have a poor dining experience. On the other hand, it is often difficult to establish an effective communication mechanism between students and cafeterias. The traditional canteen model is difficult to meet the current needs, and the upgrade of the canteen system in universities is urgent. This paper designs a set of waste reduction intelligent cafeteria system based on Raspberry Pi to reduce waste. The whole system effectively solves the problems of traditional canteens, and establishes a certain reward mechanism to encourage students to use up the purchased dishes and reduce waste. The university canteen provides a practical model for improving service quality and reform and transformation.
Design of Networked Ammunition Network Communication Scheme Based on ESP8266
Authors: Zhang Xuhuan , Li Haojie , Huang Hu , Feng Yue
Published: January 2021

This paper analyzes the collaborative combat process of networked ammunition and forms a complete expression of the functional requirements and design constraints of the networked ammunition network communication scheme. Based on the requirements for the wireless communication function of the fuze by the network cruise missile in each process, the article proposes a network node communication networking scheme based on ESP8266, and conducts a hardware construction test. After testing and verification, the network system can realize stable information transmission between different nodes and between network nodes and the host computer. This networked ammunition system also has the functions of real-time reporting of node status, data encoding and decoding, and has low power consumption and stable performance. It is suitable for application in networked ammunition combat systems.
Design of Character Recognition Robot Based on FPGA
Authors: Zhu Y , Tang Y F , Wu Y X
Published: January 2021

As the inspection tour targeted at disarranged parking phenomenon conducted by specially assigned security personnel to parking lots in neighborhoods makes it more difficult for parking management, this paper proposes an FPGA-based character recognition robot design. The camera was controlled by the master controller EP4CE115F29C7N to acquire the license plate information, the improved character recognition algorithm based on YCbCr color space was put forward to identify the license plates, and the robot walking was controlled by combining IR control with increment-type PID. The experimental results show that this robot is of certain practical value as it is capable of effective recognition and displaying the characters on the license plates.
Study on User Experience of Live Streaming Sales Based on ISM and Kano Quality Model
Authors: Feng Yu-Liang , Huang Chia-Hui
Published: January 2021

With the development of the Internet economy, live streaming sales have become a new marketing method, which is popular among users. This paper explores quality attributes of live streaming sales based on the experience of their users. In this study, 56 factors, including the original and abstract reasons for and the specific item of user experience of live streaming sales, were first obtained with the evaluation grid method according to the semi-structured interview of highly involved people. Then, 5 experts formed a focus group for discussion and determined 11 key factors influencing user experience as analysis factors of the ISM theoretical model. Results are obtained through the statistical analysis of the ISM theoretical model, and four major dimensions are defined, namely the first-level demand dimension, the second-level interaction dimension, the third-level good quality dimension and the fourth-level extension dimension. Finally, the relationship between attractive quality attributes in different dimensions and the Kano two-dimensional quality classification and user satisfaction is analyzed based on the Kano two-dimensional quality questionnaire. A satisfaction coefficient matrix diagram is drawn to analyze the key quality attributes. Research conclusions and suggestions are made according to the results of data analysis so as to provide reference value for subsequent improvement of related designs and help designers improve user experience.
Application of Improved YOLO V3 Algorithm for Target Detection in Echo Image of Sonar under Reverb
Authors: Fang Jie , Wang Pingbo
Published: January 2021

aiming at the problems of low detection rate and high false alarm rate of small and medium targets in images, a YOLO V3 improvement method is proposed and applied to the detection of small targets. Because the small target occupies less pixels and the feature is not obvious, it is proposed to up-sample the 8 times downsampling feature map of the original network output, splice the 2 times upsampling feature map with the second residual block output feature map, and establish the feature fusion target detection layer with 4 times downsampling output. In order to gain

More small target feature information is added to the second residual block DarkNe-t53 the YOLO V3 network structure. k-means clustering algorithm is used to cluster the number of target candidate boxes and the aspect ratio dimension. The results show that the improved YOLO V3 algorithm can effectively detect small targets and improve the recall rate and detection rate of small targets.
Deep learning SPECT lung perfusion image classification method based on attention mechanism
Authors: Zeng Sitao , Cao Yongchun , Lin Qiang , Man Zhengxing , Deng Tao , Wang Rong
Published: January 2021

SPECT lung perfusion is an important functional imaging technology. It can capture the functional lesions of the lung in a non-invasive manner and has become an important clinical detection method for diseases such as pulmonary embolism. In order to realize the automatic detection of the degree of pulmonary embolism, this paper studies and constructs a deep classification model based on the attention mechanism. First, the normalization technique is used to convert the original lung perfusion file into a SPECT image; secondly, in view of the over-fitting phenomenon of the deep learning model caused by the small amount of medical image data and the unbalanced data, the image translation and rotation techniques are used to perform effective expansion; then, in order to improve the model’s feature extraction ability, the attention mechanism is combined with the depth classification model to build a SPECT lung perfusion image classification model; finally, a set of real SPECT lung perfusion images were used to carry out comparative experiments on various depth classification models. The experimental results show that the model proposed in this paper can effectively detect the extent of lung disease lesions, and the classification accuracy rate exceeds 88%, which verifies the effectiveness and reliability of the classification model.
Imbalanced Thangka Image Classification research Based on the ResNet Network
Authors: Zeng Fuliang , Hu Wenjin , He Guoyuan , Yue Chaoyang
Published: January 2021

Aiming at the problem of performance degradation caused by ignoring the softmax score of the wrong class in the training process of the unbalanced data set of Thangka images and the problem of the loss of negative feature information in the propagation process of the ReLU activation function, a new loss calculation method is proposed. Firstly, the parameters of the pre-training model on the COCO data set are used as the initial parameters. Secondly, CE and CCE are used to calculate the loss during the calculation of loss in the back propagation. Finally, AReLU activation function is used and a weight assigned to CE and CCE is added as final loss to update the parameters. The experimental results show that this algorithm improves the convergence speed and accuracy of the model with respect to imbalanced data. Compared with other loss functions, ours method performance is state-of-the-art, such as complement cross entropy.
Illumination-Adapted Long-Term Tracking Based on Multiple Correlation Framework
Authors: Han Xikai , Yu Gang , Hu Han , Liu Jia
Published: January 2021

Object tracking is a hot field in image processing, among which the kernel correlation filtering algorithm is widely applied based on the advantages of precision and speed. However, due to information decay, it has poor performance in the long-term tracking. In this paper, we develop an illumination-adapted long-term tracking method based on correlation filtering tracker. Firstly, we introduce illumination adaptive normalization and information entropy weighted feature fusion to improve the prediction accuracy. Secondly, we estimate the object scale quickly based on the binary strategy to improve the speed of the algorithm. Then, we design re-detection based on online SVM detection for target loss. Finally, we design an appropriate updating mechanism to combine the whole tracking framework with various judgment thresholds. The experiment on the data set shows that all of our modules are effective and have great improvement as compared with other correlation filter type trackers.
Research on Cloud Classroom Construction of Higher Vocational Colleges Based on Campus Micro-Cloud Platform
Authors: Yin Lijing
Published: January 2021

With the rapid development and popularization of the Internet, the Internet era has arrived, and the Internet has entered people’s lives and has become an inseparable part. The system adopts RESTful architecture design and implements server-side interface to realize the unification of server interface; adopts JS (JavaScript) inter-modulation technology to realize it. The system adopts WeChat authorized login method to realize user login function. The system software is an online classroom software. Users need to be able to watch the course videos, so a player is required to play the course videos. After the experimental comparison and test, the improved server reduces the corresponding time and error rate of the system, and the instantaneous high concurrency performance of the network has been significantly improved, which can provide more stable technical support for the campus cloud classroom.
Research on Intelligent Image Recognition Technology Based on Equalization Algorithm
Authors: Qin Fang , Zeng Weijia , Ruijie Liu , Xuming Lu
Published: January 2021

In the image edge distortion correction, the straight-line projection-derived edge fit is poor, resulting in large correction errors. Therefore, an image edge distortion correction algorithm based on the equalization algorithm is proposed in the paper, where the adaptive threshold wavelet denoising algorithm is adopted to implement adaptive transformation of various scale parameters to complete noise removal. According to the results of image denoising, Fisher vector coding is used to optimize the results of the equalization algorithm to extract the edge distortion of the image. Besides, based on the edge distortion shape extraction results, the correction objective optimization function is obtained to analyze the edge fracture situation, realizing the linear projection derivative edge fitting. Meanwhile, by determining the image edge error evaluation function, the method of image edge distortion correction is judged to achieve the purpose of image edge distortion correction. Therefore, taking noisy barrel distortion and pincushion distortion images as the research object, the experimental analysis is carried out. The results show that the algorithm proposed in the paper can effectively correct the barrel distortion and pincushion distortion of the image, achieving high-precision and high-efficiency image edge distortion correction.
Innovative business model of energy Internet and construction of power grid enterprises
Authors: Wang Dawei , Wang Lixia , Li Wei
Published: January 2021

In this paper, the Internet and the power industry are deeply integrated to create a new development model, so as to realize the economic transformation from non clean energy driven to clean energy driven. Based on the theoretical framework of innovative business model, this paper adjusts the organizational structure, pays more attention to the cooperation with external institutions, so as to seek the market entrance of energy Internet, and studies the behavior of energy suppliers under the traditional business model and innovative business model from different angles.
The Electrical Properties of Single-walled Carbon Nanotubes
Authors: Liu Junzhi
Published: January 2021

Carbon nanotubes are expected to be a new type of semiconductor material to replace the traditional silicon-based semiconductor material. The high carrier mobility, ideal subthreshold characteristics and ballistic transport characteristics of carbon nanotubes can effectively solve the short channel effect, performance and power consumption limits of silicon-based semiconductor devices. These excellent properties are due to the structure of carbon nanotubes. This paper focuses on the relationship between the structure of carbon nanomaterials and their electrical properties, and the advantages and prospects of using carbon nanotubes as semiconductor materials are also described.
Application Analysis of Zinc Oxide Nanomaterials Based on Quantum Dots in Energy Environment
Authors: Xiao Yushi
Published: January 2021

Nano-zinc oxide is a new type of multifunctional inorganic material. Nano-ZnO has excellent photocatalytic performance, which can effectively convert harmful pollutants into inorganic substances such as H2O and CO2, and can effectively remove many refractory substances, thus having great application potential in pollution control. In this paper, ZnO quantum dots are selected as the research object, the nanostructures and various morphologies of ZnO quantum dots are introduced, and various methods for preparing ZnO nanomaterials and their applications in the field of energy and environment are summarized.
Designs of Q-switching Laser Power Supply with High Voltage Narrow Pulse
Authors: Zhou Ping , Ma Xiao , Shen Huaibo , Yang Xiaoyu , Zhao Chenxiao
Published: January 2021

Based on the pulse generation principle of Q-switching and cavity-dumping technologies, this paper designs a power supply with high voltage and narrow pulse for switching avalanche transistor with rising edge of ns level. In the power supply, an avalanche transistor and VOMS transistor are matched to design a driving circuit, which outputs 4.6ns narrow pulse signal. The circuit replaces the traditional cable transmission signal drive mode, reduces the whole signal transmission distance, and improves the signal transmission stability. It makes the two ends of the laser Q-switched crystal KD*P be pushed off instantaneously, which shortened the output laser pulse width from 18ns to 5.6ns. The power supply with high voltage and narrow pulse can be well used in many laser fields including communication, medical treatment, ocean exploration, etc.
A Complex Circuit Tolerance Analysis Method Based on Affine Analysis
Authors: Huo Xiangli , Ma Cong
Published: January 2021

In this paper, the selection of tolerance analysis objects and the uncertainty in the process of tolerance analysis for complex circuit systems are studied. A relatively perfect principle for determining the key circuits of tolerance analysis is formed. And considering the linear and nonlinear problems of circuits, based on the affine analysis method, a tolerance analysis method considering the influence of random factors is studied. Then the oretical basis and implement steps of the mfethod is given finally, and the application of the typical circuit in a product circuit system is verified.
The Control System of Friction Welding Machine for 5000kN Large Electrolytic Aluminum Prebaked Anode Conductor
Authors: Feng Xiaolin , Mao Haijie , Liu Chaorong , Liu Manqiang
Published: January 2021

A friction welding machine, which improves the connection performance of two different metals by generating heat of relative movement of friction, could increase the maximum contact area and lower energy and human labor consumption. The machine’s control system reaches the manufacturing process’s demands with its design and exerts its promising prospects.
Discussion about the maintenance period and spare parts of electric multiple units
Authors: Li Yongsheng , Liu Tao , Zhang Yongsheng
Published: January 2021

This article firstly discusses the problem about maintenance period of electric multiple units (EMU); Secondly, the types of spare parts and process of calculating the quantity of them during the life cycle period for 30 years are introduced. At last, the determination process of preventive maintenance(PM) and corrective maintenance (CM) spare parts is introduced in detail by taking the secondary suspension of bogie system as example.
A 2.7GHz sub-sampling phase-locked loop circuit
Authors: Xin Xiaoning , Peng Xu , Jian Ren , Qiao Wenhu
Published: January 2021

A 2.7GHz low phase noise sub-sampling phase locked loop is introduced. The low noise performance is realized by using the sampling loop and the frequency lock loop. In the locked state, only the sampling loop works, while the frequency lock loop does not work, and the frequency divider does not work. Therefore, in-band noise generated by the phase detector and charge pump will not be amplified N2, so the sampling loop’s in-band noise will be greatly reduced. In order to reduce out-of-band noise, the voltage controlled oscillator is a LCVCO with nmos-pmos complementary structure. The PLL is based on TSMC 130 nm CMOS process. Under the working voltage of 1.8V, PLL consumes 24.6mw.
Research on Optimal Method of Multi-regional Power Grid Investment Project
Authors: Li Hui , Liu Zifa , Yu Puyang , Wang Zhidong , Zhao Lang
Published: January 2021

The optimization of multi-regional power grid investment projects is the basis for ensuring the benefits of power companies and can meet the urgent needs of power grid development. This article first takes the historical benefits of power grids, planning benefits, power grid scale and development needs as indicators to establish a multi-regional power grid investment project optimization index system. Then this article puts forward the investment project optimization model. With the goal of maximizing investment benefits, with project portfolio constraints, grid scale development demand constraints and investment constraints as constraints, the binary particle swarm algorithm is used to optimize the investment project portfolio schemes of various regions. Finally, an example is used to verify the effectiveness and feasibility of the method proposed in this paper.
Influence of the Amplitude of Inlet Axial Pulsating Flow on Turbine Working Characteristics
Authors: Ji Bing , Wang Zhiduo , Li Jun
Published: January 2021

The flow field at the outlet of the rotating detonation combustor has strong pulsation characteristics, and this strong impact will bring fatal damage to the downstream turbine. In this paper, the simplified sine wave model was used to simulate the rotating detonation wave to study the characteristics of the turbine working in a pulsating flow field. The results show that the greater the amplitude of the incoming flow pulsation, the stronger the non-uniformity of the aerodynamic parameters and heat transfer parameters of the flow field inside the turbine. The increase in the amplitude of the incoming flow pulsation will aggravate the separation of the end wall secondary flow and the cascade flow, resulting in greater flow loss and reducing the working efficiency of the turbine.
The stability analysis of new power generation based on Ostrowski’s theorem
Authors: Yang Lei , Huang Wei , Zhang Dan , Li Shengnan , Xiang Chuan , Xi Xinze , He Xin
Published: January 2021

The parameter mismatch between the source side and the grid side of the grid-connected system of new energy generation is one of the factors affecting the stable operation of the grid connected system. In order to ensure the safe and stable operation of the system, the stability region of system parameters is worth studying. In this paper, a fast estimation method of stable region of impedance parameters for grid connected inverters is proposed. Firstly, the impedance model of grid-connected system in dq coordinate system is established based on the impedance analysis method, and the return matrix which is the main element influencing the stability is analyzed and deduced. Secondly, the bands containing eigenvalue loci of the return matrix are obtained based on Ostrowski’s theorem, and the stability of the system is judged by Nyquist stability criterion. Finally, the simulation results show that the proposed method can quickly estimate the stable region of the impedance parameters of the grid-connected inverter, which verifies the correctness of the method.
Abnormal network access detection of power Internet of Things terminal layer equipment based on equipment portrait
Authors: Yang Dongkai , Zhang Xiaoliang , Wu Kehe , Lu Lipeng
Published: January 2021

In the environment of the power Internet of Things, equipment network security is mainly analyzed through the physical signal characteristics of the equipment or a single flow characteristic, so as to realize the equipment network abnormality detection. Therefore, a method for detecting network abnormalities of power Internet of Things terminal equipment based on device fingerprints is proposed. Aiming at the problem of incomplete detection methods, a multi-dimensional matching device fingerprint model is established. Firstly, the basic information of the device is collected, such as IP, MAC, etc.; then the network traffic information of the device is analyzed to extract the characteristics of network traffic; then, the frequency of keyword and keyword group of service protocol is counted. The device fingerprint model is established by using the traffic and service characteristics. When the network anomaly occurs, the device fingerprint model can be found effectively. The experimental results show that the device fingerprint model can detect the abnormal behavior of the device.