bluuebunny/crossref_metadata_2025 huggingface dataset:
Construction of Intelligent Logistics System Based on Internet of Things Technology
Authors: Rong Tan , Hulin Zhai
Published: April 2021

With the promotion of information technology, the Internet of Things develops rapidly and is gradually applied in all walks of life. Intelligent Logistics has become an inevitable trend in the development of logistics industry. This paper expounds the related concepts of the Internet of Things and Intelligent Logistics, introduces the key technologies in the Internet of Things, and constructs the Intelligent Logistics system based on the hierarchical structure and key technologies of the Internet of Things, so as to promote the sustainable development of Intelligent Logistics.
Modeling and security analysis of CBTC signal failure scenario based on ScOLA
Authors: An Xiaotong , Zou Jinbai , Shen Zhukai , Yao Shunyu
Published: April 2021

For the entire phase of the design, manufacture and operation of the Communication Base Train Control (Communication Base Train Control, CBTC) signal system, safety is the primary issue that needs to be considered. This article introduces ScOLA’s modeling language, combined with the situation of the wireless loss fault of the vehicle-mounted signal equipment, and carries out a formal description and step-by-step simulation of the system. It further illustrates that the application of the ScOLA modeling language can perform the safety analysis of the CBTC signal system faster and more accurately.
Meta-path Guided Heterogeneous Graph Neural Network For Dish Recommendation System
Authors: Zhang Jiarui , Zhu Yonghua
Published: April 2021

The traditional recommendation system can hardly utilize the ingredients and flavor characteristics of the dishes, and faces the problems of sparse data and cold start, resulting in inaccurate results of the recommendation system. This paper addresses these problems by constructing a heterogeneous graph network with the interaction data between users and dishes in the food domain and taking the main and auxiliary ingredients as nodes. Also, we capture the higher-order structural information among users, dishes, and main and auxiliary ingredients by using meta-path guidance. In the meantime, we assign weights to the edges associated with user nodes and main and auxiliary ingredients nodes, which can obtain users’ preferences for main and auxiliary ingredients by using weighted GCN networks. The experiments conducted on a food domain dataset demonstrate that the meta-path-guided heterogeneous graph dish recommendation algorithm proposed in this paper is improved over the traditional recommendation algorithm.
Research Hotspots Mining and Trend Analysis of Blockchain
Authors: Fang Ji , Tu Wenyan , Jiao Fangyu , Ci Yutong
Published: April 2021

In this paper, the bibliography included in Web of Science is taken as the data sample. This paper analyzes articles, reviews and proceedings papers published from 2013 to 2020 in the field of blockchain to reveal the major countries and institutions. We use co-word method to identify research hotspots in the field of blockchain. The results show that the research on blockchain is an emerging research area. China and The United States are always in the leading position. Beijing University of Posts & Telecommunications publish most papers in the field of blockchain. Smart contract, Internet of Thing, bitcoin, security and privacy are the hottest research topics in the field of blockchain.
Research on Unknown Threat Detection Method of Information System Based on Deep Learning
Authors: Zhang LingHao , Liang Yunhui , Tang Yong , Wang Seng , Tang Chao , Liu Chun
Published: April 2021

Enterprise information system may be threatened and attacked at any time from the external and internal environment, so how to improve the enterprise information system from the outside world unknown threat detection is particularly important. This paper is based on the method that is applied to detecting and identifying unknown threat, so as to protect and improve the information system’s safety and stability with technical support and guarantee. It can avoid the impact of unknown threat on information system security to the greatest extent.
Research on Control System of Delta Robot Based on Visual Tracking Technology
Authors: Wu Hongxia
Published: April 2021

Delta parallel robot has the advantages of fast motion speed, accurate positioning, low cost and high efficiency. In this paper, OMRON Adapt Hornet 565 3 axis parallel robot is taken as the research object and the control system of Delta robot based on visual tracking technology is studied. The control flow such as camera configuration, model configuration, and process configuration is described in detail, the control system was tested finally. Through the tests, The parallel robot can grasp different shapes of models accurately and quickly under the action of visual tracking.
An Automatic Parking Model Based on Deep Reinforcement Learning
Authors: Junzuo Li , Qiang Long
Published: April 2021

When parking a car, it is crucial to ensure the car constantly approaches the parking point, gets an excellent heading angle, and avoids significant losses caused by line pressure. An automatic parking model based on deep reinforcement learning is proposed. A parking kinematics model is built to calculate the different states of its movement. Steering angle and displacement are used to achieve interaction as actions; A comprehensive reward function is designed to consider the focus of action and safety in different parking stages. Through training, the car’s automatic parking is realized, and a comprehensive analysis of the various stages and situations in the parking process is given. Besides, it is showed by a further generalization experiment that the model has good generalization.
Image Denoise Methods Based on Deep Learning
Authors: Wu Changhe , Gao Tianhan
Published: April 2021

Image denoising is widely used in image, video, nuclear magnetic imaging and so on. In the application scene, camera jitter, the rapid motion of objects, dark light environment and so on may cause the captured photos to be unclean, so the research of image denoising has essential research value. This paper reviews the related research in this field in recent years, introduces the basic theory of image denoising, lists the common image noise, and then summarizes some classical denoising algorithms from traditional denoising methods. In addition, the shortcomings of traditional methods are analyzed. After that, the image denoising method based on depth learning is summarized, including the image denoising method based on REDNet, DnCNN, CBDNet, GAN, Noise2Noise structure, the principle and structure of various methods are introduced. Finally, the challenges of image denoising are analyzed, and the future research direction has prospected.
Research on Network Big Data Mining Technology Based on Structured Similarity
Authors: Wang Lei
Published: April 2021

At present, most institutions in my country mainly use clustering algorithms for data mining activities, but low accuracy is one of the common problems they face. The article proposes a network big data mining technology based on structured similarity. It is to convert the target data set in the network system into nearest neighbor network (kNN). It then use the network big data mining technology based on structured similarity. The target data is clustered to obtain a big data result with a slightly poorer objective function but higher clustering accuracy, so as to meet our needs for accurate data statistical results.
Research on FT-IR spectrum data mining of diesel engine lubricating oil
Authors: Li Jing , Tian Hongxiang , Ming Tingfeng , Sun Yunling , Zhang Shuai
Published: April 2021

Aiming at the difficulties in quantitatively describing the pollution component information contained in FT-IR spectral data of diesel engine lubricants, wavelet transform and fractal box dimension are used to process FT-IR spectral data and get fractal dimension curve. Multiple peaks can be obtained by analyzing the curve. Two groups of lubricating oil samples with water content of 0.10%, 0.22%, 0.44% and 0.88% respectively and with ethylene glycol content of 0.10%, 0.22%, 0.44% and 0.88% respectively. For the above two groups of oil samples, the corresponding relation between peak position and contaminate ratio can be obtained, and a new method based on FT-IR prediction of contaminate ratio is feasible.
Neuron classification with a data-driven workflow
Authors: Li Longfei
Published: April 2021

The shape feature of a neuron reveals the behavior of a neuron cell. Usually, a set of features of neuron anatomy can define the morphological characteristics of a neuron for comparison or analysis of a group of neuron cells. At present, the best set of neuron morphological description characteristics cannot be determined in the field of brain science research. Based on a data-driven strategy, this paper establishes a workflow from neuron feature processing to morphological classification.
Fuzzy Weighted Entropy Attention Deep Learning Method for Expression Recognition
Authors: Yao Lisha , Zhao Haifeng
Published: April 2021

Deep learning network has been widely used in facial expression recognition. However, due to the complexity and variability of expression images and the influence of various factors such as illumination and individual differences, the recognition effect of existing methods needs to be improved. In order to improve the expressive ability of deep learning network, an attention mechanism based on fuzzy weighted entropy is introduced into deep learning network and applied to multi-channel facial expression recognition. Firstly, the convolutional neural network model is constructed to extract the convolutional layer multi-channel features of facial image. Then, the fuzzy weighted entropy of different channel features is calculated as the attention weight. Finally, the attention weight and multi-channel features were fused to extract the high-dimensional features of the facial expression image and sent to the full connection layer to complete the facial expression classification. Experiments in CK+ and JAFFE databases show that the average recognition rates of this method obtained 94.86% and 96.88%. It is better than the mainstream methods in recent years.
RETRACTED: A Probe into the Professional Development Path of College English Teachers under the Background of Big Data
Authors: Zhang Ping
Published: April 2021

the concept of big data originated in the field of computer Internet, with people’s increasing understanding of big data, a new diversity of data and information assets has arrived in an all-round way. The arrival of the big data era has not only brought a profound impact on the field of education, but also brought new changes to college English teaching. By combing the current situation and characteristics of college English teachers’ professional development and the path of college English teachers’ professional development under the background of big data, this paper probes into the future professional development of college English teachers, so as to improve the teaching effect and students’ learning ability.
Point of Interest Recommendation Based on Graph Convolutional Neural Network
Authors: Zhai Fubo , Li Baozhu
Published: April 2021

The rapid development of the mobile Internet makes location-based social networks (LBSNs) play an increasingly important role in practical applications. Among them, point of interest(POI) recommendation is a research hotspot in the current context. As a kind of graph data, social network can naturally express the data structure in real life. In view of the current POIs recommendation research ignoring the diversity of graph data, we proposed a POI recommendation based graph convolutional neural network (PBGCN) model, which used the check-in information, popularity characteristics of interest points, and users’ social behaviors to recommend interest points through graph convolutional neural networks(GCN). Compared with other latest recommendation methods, our model has improved accuracy. This proves the feasibility of GCN in point of interest recommendation.
Lip Reading using CNN Lip Deflection Classifier and GAN Two-Stage Lip Corrector
Authors: Zhang Chengwei , Zhang Senyao
Published: April 2021

Lip reading is a task to infer the content of speech through the movement of the lips, which is a technology with wide application prospects. Thanks to the development of deep learning technology and the proposal of large-scale datasets, lip reading has made great progress. However, these large-scale datasets contain only small angle lip deflection which is different with the wild environment. Most networks based on these datasets take some simple methods to correct the lip deflection that led to performance degradation when in the wild. In this work, we proposed a very challenging dataset with large angle lip deflection and multiple speakers to simulate the wild environment, and we designed a lip deflection classifier based on Convolutional Neural Network and a two-stage corrector based on Generative Adversarial Network. We use these two models to correct the lip deflection and improve the recognition accuracy. Our proposed method achieves an absolute improvement of 18.3% and 7.4%, respectively, compared with no preprocessing and just face alignment, which shows that our proposed network is more suitable to correct the large angle lip deflection in the wild.
Research on Lightweight Improvement of Sonar Image Classification Network
Authors: Zhou Yan , Chen Shaochang
Published: April 2021

According to the constructed sonar common target detection dataset derived classification dataset, the convolutional neural network is used to classify the target. And in order to be able to better apply actual engineering, the lightweight of the network has been studied in depth. First, the performance of the ordinary convolutional neural network VGG-16 and the lightweight convolutional neural network MobileNet on the derived classification dataset is compared. The results show that the lightweight network achieves better results at a smaller cost. Then use the dilated convolution and bottleneck structure to improve the MobileNet network to obtain the IMDNet network and the IMBNet network. Through experimental comparison and analysis, the two improved networks have a significant reduction in the amount of parameters and calculations compared to the MobileNet network. The performance of the IMBNet network is basically the same as that of the MobileNet network, and the accuracy of the IMDNet network is 2.02% higher than that of the MobileNet network. This shows that in the field of sonar image classification research, lightweight convolutional neural networks have good performance, and have certain practical application prospects and values.
Traffic Crash Severity Prediction with Deep Learning
Authors: Chen Xining
Published: April 2021

This paper is based on deep learning to explore the accuracy forecasting model of traffic crash severity. A prediction model in terms of convolutional neural networks is proposed in the paper. The first is to collect the basic information of all traffic crash data of the entire urban road network in Chicago from 2016 to 2020. Then using the PivotTable to observe the distribution of each variable, and filtering them. The required prediction model is based on Python and it mainly includes three parts, the input layer, the hidden layer, and the output layer. Besides, using SGD optimizer to accelerate the training of the network model is a need. Aiming to avoid problems such as over-fitting, the dropout technique is adopted, and its parameter value taking 0.1 is better. Then comparing and analyzing with three traditional machine learning models, the decision tree classification prediction model, the logistic regression prediction model and the support vector machine prediction model, it is obvious to show the better performance of prediction model based on the convolutional neural network method. After optimization, its highest accuracy is 89.2%.
A Deep Learning Method for Chinese writer Identification with Feature Fusion
Authors: Xu Yang , Chen Yuehui , Cao Yi , Zhao Yaou
Published: April 2021

Writer identification is one of the research hotspots of computer vision and pattern recognition, and it is of great significance in the fields of judicial authentication, file security protection, historical document analysis, and so on. However, many problems are still challenging due to the different writing sources, the common features of learning local features, and the implicit features of handwriting style. This paper uses the fusion of depth features and manual features to obtain handwriting style features from handwriting pictures. Firstly, the handwriting picture is pre-treatment and divided into small pixel blocks, and then the depth feature and LPQ feature information are extracted from each pixel block, and the depth features are reduced by PCA, and then the local features are encoded into global features through the Vlad algorithm. So far, the depth global feature and the LPQ global feature of a page of handwriting pictures are obtained, and the two features are combined as the global feature vector of the page of handwriting. And our method has achieved good results on the CASIA-HWDB data set.
Research on the Construction of Smart Cities by the Big Data Platform of the Blockchain
Authors: Ma Qianwei , Yang Yanxia
Published: April 2021

In the process of rapid development of information technology, to promote the further construction of new smart cities, comprehensive utilization of cloud computing technology, big data technology and Internet of Things technology will help improve the convenience of residents’ lives. The development of blockchain technology provides more possibilities for the construction of new smart cities. The use of blockchain and other new technologies to build a smart city big data platform can realize a point-to-point communication mechanism. At the same time, this can also establish a more reliable trust agreement. It is conducive to the realization of applications such as smart IoT, government affairs, and big data. Using the blockchain big data platform to build a new type of smart city can break information islands and achieve the purpose of data sharing and information security. This is a key issue that must be paid attention to in the current construction of new smart cities. In this link, it is necessary to strengthen the research and analysis of blockchain technology to understand the problems that exist in the process of building a smart city on a big data platform. This article discusses the application advantages and related strategies of a smart city big data platform based on blockchain technology.
The Development Tendency of Artificial Intelligence in Command and Control: A Brief Survey
Authors: Niu Yukai , Jin Xiaoxi , Li Jinhui , Ji Gang , Hu Kai
Published: April 2021

Google’s DeepMind’s AlphaStar has achieved great success in the real-time strategy game StarCraft 2, and it has also made the combination of artificial intelligence and command and control, attracted widespread attention. Artificial intelligence theory and its engineering practice accumulated over many years have injected new development momentum into traditional fields, such as command and control. We download more than 900 high-level papers from Web of Science (WoS) that combine artificial intelligence and command and control as the research object, and employ bibliometric, CiteSpace, VOSviewer, and other bibliometric analysis software. From the number of documents, co-citing, co-occurrence of keywords and research hotspots, the four aspects are studied and visualized analysis, which clarifies the research status and key documents of artificial intelligence in command and control, and reveals the frontier hotspots and research directions in this field. Finally, a summary of the four visual analysis aspects points out important references for choosing research directions, exploring the frontiers of disciplines, and assisting scientific decision-making in this field.
A deep learning model for positioning perforated stent
Authors: Wang Erhui , Zheng Liancun
Published: April 2021

How to determine the window position of the aortic stent is an important issue in cardiovascular disease research. Currently, this process is viewed in two dimensions, and a lack of information is prone to errors. Here, the real-time frame is suggested to enumerate the three-dimensional shape using the two-dimensional perspective image. A robot-assisted window-opening stent implantation with deep learning is geometrically aligned ratios in window-opening aortic endovascular repair. First, place markers during the windowed stent. Then, we located the three-dimensional pose of each stent segment. Third, the overall three-dimensional shaped stent-graft is achieved by graft gap in terpolation. It has been proposed to separate the markers from the two-dimensional fluorescence microscope for semi-automatic label detection of the image. In this way, we can better understand the opening position of the aortic stent.
A Study on Website’s Column Optimization by User Preference Analysis: Taking “Donghu Circle” as an Example
Authors: Yan Jingjing , Yan Yu , Cao Sheng
Published: April 2021

In the era of big data, the development of localized information platform has made a certain breakthrough, which can be operated and optimized with the help of data analysis and around user preferences and column classification. Through the user’s use track and browsing preference on the platform, the reaction degree of users to different column content is explored, and the most concerned and interested sections are found, so as to optimize the column classification and content filling.
Research on the Method of Information System Equipment Performance Analysis Based on Big Data
Authors: Zhu XiangLei , Lin Pan , Liu Yalei
Published: April 2021

As the business scale of the information system continues to increase and refined management, the increase in the load of the information system has become increasingly obvious, and the requirements for the reliability and stability of the information system are getting higher and higher. In order to improve the reliability and stability of the information system, a safe and stable performance analysis system must be established. Therefore, first obtain performance indicators such as throughput and response time of the information system. Secondly, establish a performance baseline between load, response time, and resource consumption. At the same time, it is necessary to introduce time, external time and other parameters for big data analysis, and finally conduct a situation assessment of the information system.
Design and Implementation of Basic Data Platform for University Library
Authors: Fan Zehua , Song Jiao
Published: April 2021

Considering the problems existing in the data management and use of university library, this paper puts forward the concept of constructing “basic data platform”, which can provide the guarantee for the production and business data of library and university. The system includes data acquisition system, library data exchange system, data access API interface, cold data transfer system, data visualization and management platform, data analysis and mining modules, forming a complete system of data storage, management and use.
A Study on Data Monitoring and Effect Optimization of Programmed Advertising Platform: Taking “Ocean Engine” as an Example
Authors: Yan Yu , Cao Sheng
Published: April 2021

Currently, programmatic advertising has become the most popular way to purchase online media in our country. More and more Internet companies are beginning to use their own data and technological advantages to build their own programmatic advertising service platforms. This article takes the programmatic advertising service platform of Bytedance Company-the “Ocean Engine” as the research object, and explores the strategy of data collection and effect optimization of programmatic advertising.
Research on the application of big data technology in college students’ innovation and entrepreneurship guidance service
Authors: Yuan Fang , Hu Die
Published: April 2021

The concept and mode of innovation and entrepreneurship education are embedded in the research university education system. From the four dimensions of external policy and public opinion environment, internal system module, operation mode and operation mechanism, this paper constructs the framework model of innovation and entrepreneurship education system in research universities. The model not only conforms to the training law of innovation and entrepreneurship talents, but also conforms to the training law of innovation and entrepreneurship talents. At the same time, innovation and entrepreneurship education can also be incorporated into the university education system. It also focuses on the internal system module of innovation and entrepreneurship education and the operation mode of three shift system. It lays a theoretical foundation for the promotion and implementation of the concept of innovation and entrepreneurship education in China. The relevant achievements of the construction of the innovation and entrepreneurship education system are optimized and improved accordingly. After putting it into practice, the innovation and entrepreneurship education level of our universities will be greatly improved.
Data visualization project implementation workflow of smart cold chain system
Authors: Zeng Xi , Liu Shuxian , Chen Yanru
Published: April 2021

Purpose: In response to the increasingly complex cold chain related work, the requirements for daily cold chain system data information collection, analysis and data information traceability continue to increase. This article starts from a design perspective, research how to improve the effectiveness and usefulness of data and information transmission in the cold chain system to build a smart cold chain system. Method: By mining the connection between smart cold chain and data visualization, analyze the goals and requirements of data information transmission and reception. The design uses a large data visualization screen as the carrier, mainly research the design process of data visualization large screen, it is used to construct the actual operation of the data visualization project of the smart cold chain system. Conclusion: Data visualization has great advantages in mining, disseminating and communicating data information. Combine the smart cold chain system with data visualization, provide effective data information for workers in the smart cold chain industry, highlight functions required to upgrade the smart cold chain system, consolidate the coordination between the various working links of the smart cold chain, strengthen the user experience of the staff related to the smart cold chain, improve the efficiency of smart cold chain work.
Deep learning for skeleton-based action recognition
Authors: Ye Lei , Ye Shiqing
Published: April 2021

In order to further improve the recognition rate of skeleton action recognition, and to break the limitation that most of the previous deep learning-based methods have the input content of human joint coordinates, we propose a human skeleton action recognition algorithm combining geometric features and LSTM networks. In this paper, we propose a human skeleton action recognition algorithm that combines geometric features with LSTM network. The algorithm selects geometric features based on the distances between joints and selected lines as the input of the network, and introduces a time-selective LSTM network for training. The ability to select the most recognizable temporal features using the time-selective LSTM network is demonstrated in the SBU Interaction dataset and UT Kinect dataset, achieving 99.46% and 99.30% recognition rates, respectively. The experimental results demonstrate the effectiveness of the method for human skeleton-based action recognition.
Application of Virtual Reality Technology in the Health Field Based on the Background of Big Data
Authors: Li Yizhou , Deng Kesui
Published: April 2021

With the development of big data and artificial intelligence, virtual reality technology has penetrated into every aspect of daily life. Among them, the application of virtual reality technology in the health field is of great significance. Virtual reality technology can assist clinical medicine in the treatment of patients, and can also simulate the physical feelings of panic disorder and some disabilities to induce emotions similar to patients, produce empathy effects, enhance normal people’s understanding of patients, or improve health The effect of dissemination. This article discusses the current status of the application of virtual reality technology in the context of big data, and analyzes how virtual reality technology can promote health communication, disperse pain, treatment of psychological disorders, and begin to provide services for the elderly, which has great social and practical significance. This article reviews and compares the recent applications of virtual reality technology in the health field at home and abroad, and analyzes its prospects and shortcomings.
Research of UAV Controller-pilot Data Link Real-time Processing Technique
Authors: Chanying Qi , Yuchao Li
Published: April 2021

In traditional flight tests, ground monitoring is based on on-board telemetry data. With the rapid development of UAV design, the monitoring data source transitions from radio telemetry transmission to ground-to-air data link transmission gradually. Traditional ground real-time processing system can’t meet the new requirements. UAV controller-pilot data link real-time processing technique doesn’t rely on traditional PCM telemetry, via controller-pilot data link which is integrated into the UAV, ground controller system and flight tests data processing system, which promotes the efficiency of flight test dramatically, the data processing duration is also reduced significantly.
Intelligent recognition of surface defects of parts by Resnet
Authors: Wang Fushuai , Qiu Jiyuan , Wang Zhefeng , Li Wenrui
Published: April 2021

Nowadays, Automatic metal surface defect recognition is an important research direction in the field of surface defect recognition, and more convolution neural network algorithms are applied in this field. However, with the deepening of network layers, network degradation will occur. We propose a ResNet method for classifying metal surface defects. After experimental testing, we use ResNet34 to build an identification network. After training 300 epoch of the network using the NEU surface defect data set, the convergence of the network is very good. The accuracy of test set verification is 93.67% and higher than that of other surface defect recognition algorithms. Also, we can deepen the number of layers ResNet the network without worrying about network degradation.
Study on the correlation between grain size and processing limit in abrasive flow machining
Authors: Yinguang Li , Guizhen Song , Bowei Hou , Bowen Han , Jianming Cheng
Published: April 2021

The influence of particle size on the abrasive flow machining mechanism and processing limit was studied by establishing the processing model of different particle size. When many scholars studied the mechanism of abrasive flow machining in the past, they only established a machining model and ignored the effect of abrasive particle size. By comparing the size of different granularity abrasive particles and the workpiece surface in contact with each other, the large granularity abrasive particle processing model and the small granularity abrasive particle processing model were put forward, and their processing mechanism was analyzed respectively. On this basis, the reason of machining limit in different particle size abrasive processing process is explained. Finally, the theoretical analysis and experimental results were compared to verify the correctness of the processing mechanism and theoretical analysis of processing limit of abrasive with different granularity, and an optimal experimental scheme was proposed to break through the original processing limit.
Design of LQR Excitation Controller of Synchronous Condenser in HVDC System
Authors: Li Hao , Wang Honghua , Lu Hangtian , Wang Chengliang
Published: April 2021

As a kind of reactive power compensation equipment,a synchronous condenser is widely used in High Voltage Direct Current(HVDC) transmission system due to its small influence on the regulation ability and high strong excitation ability. In this paper,the practical state space model of the synchronous condenser is established and the parameters of the model are calculated. The linear quadratic regulator ( LQR) Controller for excitation system is designed and the controllability and stability are analyzed. In order to verify the voltage support ability and dynamic response performance of LQR excitation controller,the simulation is carried out in MATLAB/SIMULINK. The simulation results show that the grid voltage fluctuation can be quickly quelled and the strong excitation function of synchronous condenser is quickly exerted.
CarfRCNN: A Two-stage Effective Model For Instance Segmentation
Authors: Chen Yujie , Zhao Pengbiao , Chen Julin
Published: April 2021

With the rapid development of deep learning, many instance segmentation models have achieved good results in accuracy and time. But here are still many problems. In this paper, we proposed a two-stage model CarfRCNN. We proposed CAResNet to change the structure of the backbone, making the feature extraction of the input image more refined. At the same time, we also added the CRF module to add smooth constraints to the pixels, so that the segmentation mask fits the target contour more closely. We train and test on the COCO datasets, and compare with the Mask R-CNN. The experimental results prove that the model we proposed can greatly improve the accuracy.
Development of Virtual disassembly and assembly platform for marine air compressor
Authors: Wei LIU
Published: April 2021

The use of virtual reality technology to assist equipment maintenance and skills training has a broad application prospect, but the existing systems are often lack of sense of reality, over-simplified because it’s difficult to establish the disassembly process model, the lack of 3D interaction design, etc. Taking marine air compressor as an example, this paper analyzes the functional requirements of the virtual disassembly and assembly platform, studies the key technologies of multi-source data integration, 3d modeling with high sense of reality and high complexity disassembly process modeling, uses Maya/Unity3D and other software to realize relevant technologies and functions, and gives the development process of the system. The developed system has been applied in many maritime institutions in China. It has been demonstrated with reference value and application prospect.
Quality by Design (QbD): Application of Comprehensive Risk Analysis in Blending Process for XLGB Capsule in Medicine Industry
Authors: Zuo Yamin , Huang Jie , Deng Xuehua , Sun Yichun , Wu Qing
Published: April 2021

Intelligent development is becoming an inevitable choice for the development of green manufacturing enterprises, improving the medical quality and guaranteeing the safety is an important role player in the intelligent development of medical industry. Since the risk model prediction is crucial in many areas of medical work, risk management and communication are meaningful in industry management. The framework of comprehensive risk management of Chinese Patent Medicine (CPM) has been presented and constructed at presence. To enhance the quality level and safety management of medical service, this article explored the different theory meanings of innovation and conducted a comprehensive risk evaluation of Chinese medicine industry. This paper provides a foundational reference for future research that how to apply quality-by-design (QbD) approach for the development of medicine industry. This study demonstrates that the application of the QbD concept is a novel area in CPM quality.
Dynamic Scheduling Method of Production Plan in Automobile Manufacturing Workshop
Authors: Ren Nver , Li Maoying
Published: April 2021

In order to adapt to the needs of dynamic adjustment of production plan between different automobile manufacturing workshops and deal with emergencies, a dynamic scheduling method of production plan based on genetic algorithm is proposed in this paper. According to the requirements of different workshops, multi-layer fitness functions are designed, buffer zones are set between workshops, and the production planning scheduling is dynamically adjusted by sliding window. The experimental results show that the dynamic scheduling method proposed in this paper has the advantages of high efficiency and adjustment.
Part point cloud segmentation method based on model registration
Authors: Si Lei , Yang Han , Li Zhongming , Duan Liming
Published: April 2021

In order to analyze the machining errors of parts more accurately, a point cloud segmentation method based on model registration was proposed for mechanical parts. First, the surface information of the standard model of the part is extracted from the Initial Graphics Exchange Specification (IGES) file; then, the part point cloud is registered to the standard model using the method of the moment principal axis method and the Iterative Closest Point algorithm (ICP); then, by calculating the distance between each point in the point cloud and each surface in the standard model, it is used to determine the corresponding relationship between the model surface and the point cloud, and then the point cloud is segmented; finally, the segmentation results are surface-fitted and benchmarked to obtain a segmentation with better alignment quality. The experimental results show that this method can automatically segment the part point cloud according to the surface features of the part when only the IGES model and the point cloud model are input, without setting any empirical parameters, and obtain the segmentation result with clear boundaries.
Few-shot learning approach for 3D defect detection in lithium battery
Authors: Wu Ke , Tan Jie , Li Jingwei , Liu Chengbao
Published: April 2021

Detecting the surface defects in a lithium battery with an aluminium/steel shell is a difficult task. The effect of reflectivity, the limitation of acquiring the 3D information, and the shortage of massive amounts of labelled training data make the 2D detection method hard to classify surface defects. In this work, a few-shot learning approach for 3D defect detection in lithium batteries is proposed. The multi-exposure-based structured light method is introduced to reconstruct the 3D shape of the lithium battery. Then, the anomaly part of the 3D point cloud is transferred into 2D images by the height-gray transformation. The MiniImageNet datasets are used as the source domain to pretrain the Cross-Domain Few-Shot Learning (CD-FSL) model. The accuracy in our experiment result is 97.17%, which means that our method can be used to classify the surface defects of the lithium battery.
Research on the Stability of an Air-to-Air Missile Compound Control System with a Pulse Modulator
Authors: JianQi Wang , DongMei Wang , Liang Yan
Published: April 2021

Based on pulse modulator, a method of lateral force and aerodynamic compound control system design in air-to-air missile and the stability is studied. First, the lateral force is assumed to be continuous and blended with aerodynamic fins accordding to a certain proportion, and the compound control is designed based on three-loop control method. Then the switch order of direct force device is obtained based on the pulse modulator. At last, the stability of control system is analysed based on describing function method, and then the parameter of pulse modulator is properly chosen. Simulation results show the validity of the proposed design.
Design of Motor Operation Monitoring and Cooling Control System
Authors: ya xian Zhang , huai feng Cui , yuanchang Lin , zhen quan Liu , shi yun Wu , guotian He
Published: April 2021

Combined with PLC control technology, a motor monitoring and cooling system is designed. The main function of the system is to remotely control the motor and monitor the working environment and operating status information of the motor through the mobile phone app, and to cool the motor through the cooling system; it can effectively improve the operating efficiency and reliability of the motor, and improve the user experience. The article puts forward the design scheme of the motor monitoring system and summarizes the various functions of the system control. It also analyzes and describes the realization method and key technology of the control system in detail.
Mechanical Safety Risk Analysis of Smart Factory
Authors: Cheng Zhou , Likai Ju , Min Cao , Xiangyan Zhang
Published: April 2021

In view of the mechanical safety risks of industrial robots, AGVs and other mobile devices that are widely used in smart factories, a mechanical safety risk analysis of smart factories method is carried out. First, it analyzes the mechanical safety risks of CNC machine tools, industrial robots, AGVs, and warehouses in smart factories. Then, a specific seal production workshop is taken as an example. It conducts a mechanical safety risk analysis and gives a safety plan to reduce the risk. The mechanical safety of the personalized seal workshop has achieved sufficient risk reduction. This article can provide a certain reference for conducting mechanical safety risk analysis during the construction of smart factories.
Scheduling optimization of aging employees considering physical load
Authors: Zhou Jian , Wang Rentu
Published: April 2021

Considering the influence of age on the task completion time of the workforce, the scheduling problem of the workforce under physical load was extended, and a mathematical optimization model was proposed, preventing the workforce from continuously engaging in high workload work in the same body part and minimizing the standard deviation of task completion time. The influence of age on the workforce's reaction and endurance ability was discussed and formulated reasonably. The high and low workload of the workstation was evaluated by Rapid entire body assessment. A particle swarm optimization algorithm was designed by permutation coding based on set rules, and the rationality of the model and the effectiveness of the algorithm were proved by an example. The results show that the model can not only eliminate the risk of continuous high workload but also improve the performance of the assembly line.
A Novel Self-adjusting Ultrasound Transducer Stabilized Elastic System Realized by 3D Printing
Authors: Wu Dan , Lu Wenhuan , Li Yuxuan , Shi Haiqiang , Wei Jianguo
Published: April 2021

The human tongue is a significant organ for swallowing and speaking. The measurement of tongue function is challenging because it is hidden inside the mouth. Ultrasonic imaging is often used to study and record tongue movement. In order to ensure the validity and authenticity of the collected data, numerous methods are proposed to stabilize the transducer. However, in the long-term data acquisition, few devices consider issues such as whether the jaw is in compression during speech and whether the participant can autonomously complete the adjustment, which leads to data frame discarding and experimenter discomfort. This paper describes a novel self-adjustable ultrasound transducer stabilization elastic system using 3d printing technology that allows free head movement and flexible jaw movement. The system performance is evaluated quantitatively. It shows that in the 95% confidence interval, the average translational offset of the transducer relative to the head is 2.44mm and the rotation angle is 3.21°. Furthermore, the small head constraint makes the voice close to its natural state and more suitable for scientific studies of voice and swallowing.
Attitude control of self-balancing vehicle based on sliding mode variable structure control
Authors: Le LIU , Li-hua XING , Zhang-jun SUN
Published: April 2021

The two wheeled self balancing vehicle belongs to a typical nonlinear system. Aiming at the problem of attitude control stability of two wheeled self-balancing vehicle. A new sliding mode reaching law of integral sliding mode variable structure control was proposed. The feasibility and effetivenes of the controller are proved by theory and experiment. The experiment results show that the designed controller has remarkable advantages of steady state characteristic,fast dynamic response and strong robustness to external disturbance to compared with traditional PID controller.
Simulation installation space of the vehicle to solve the problems of component layout, electrical wiring and wire routing
Authors: Fedorov E , Ferenets A , Dudka N , Snegurenko A
Published: April 2021

The presented work is devoted to modeling the mounting space of a vehicle onboard electrical equipment complexes for solving the problems of assembling elements, wiring of electrical circuits and routing of wires. A model of the mounting space of the onboard electrical equipment complex has been developed. Various modifications of on-board electrical equipment of modern vehicles under various restrictions are considered and investigated.
Neural network architecture choice for modelling various configurations power system
Authors: Kavalerov B , Kilin G , Suslov A
Published: April 2021

Gas turbine power plants based on converted aircraft engines have been successfully produced in Perm city since 1991. Their power range is steadily expanding. Such gas turbine electro power stations have significant advantages. However, the need to make full use of these advantages requires power station control processes significant intellectualization. Intellectualization will provide an opportunity to improve the electricity quality, increase the power supply reliability and improve environmental friendliness. To achieve these goals, it is necessary to study the behavior of gas turbine power electro station in different operation modes and in various configurations power system. Such research can only be carried out on the basis of mathematical models. Moreover, these models should be high-speed, since the research of control algorithms requires a large number of various experiments in a limited time. One of the promising ways to obtain such mathematical models is to use the artificial neural networks apparatus. To construct a gas turbine power electro station mathematical model, it is necessary to choose such a neural network architecture that will allow obtaining a model for an acceptable period of time and will allow modeling the processes with the required accuracy. Therefore, the problem of choosing the neural network architecture is an urgent research priority. On the basis of the performed studies, the architecture of a neural network for modeling various configurations power system is proposed and substantiated.
Simulation of three-phase induction motor different bearing faults in Matlab Simulink environment
Authors: Salnikov S , Solodkiy E , Vishnyakov D
Published: April 2021

The article describes the simulation of bearing failures in a three-phase induction motor in the Matlab Simulink environment. According to statistics most of induction motor faults happens due to bearings destruction or stator winding insulation degradation. Vibration analysis is often used to diagnose the bearings condition. This paper considered the model designing principles of different types of bearing faults of induction motor.
Common approaches in vector control design of induction motor and permanent-magnet syncronous motor
Authors: Solodkiy E , Salnikov S , Vishnyakov D
Published: April 2021

The article describes a way to generalize current control loop structure for AC drives, by implementing general PI gains calculation block. Article contains block diagram of generalized current control loop for smooth-air-gap and salient-pole machines. Electromagnetic torque estimator is represented, whose design based on stator voltage and current measurements. Results of simulation modeling of electromagnetic torque estimator in pair with field-oriented control are represented.