bluuebunny/crossref_metadata_2025 huggingface dataset:
Machine Vision Based on Pipe Joint Surface Defect Detection and Identification
Authors: Zhu Li , Bao Xukang , Liu Shousheng
Published: September 2020

In the production process, pipe joints often have surface defects such as cracks, pits, bumps, etc., and the manual detection efficiency is low and the cost is high. This paper presents a machine vision based method for detecting surface defects of pipe joints. The preprocessing algorithm, image initial detection algorithm, image segmentation algorithm and feature extraction algorithm of tube joint surface defect image are studied and BP neural network classifier is designed. Finally, the designed classifier was tested by 300 samples of cracks, pits and bump defects on the surface of the pipe joint. The experimental results show that the recognition rate of the classifier reaches 95.5%, which can better meet the surface of the pipe joint. Defect detection requirements.
Application of an Improved Synthetical Semantic Similarity Method in Water Knowledge Graph
Authors: Gao Kaili , Yan Jianzhuo , Yu Yongchuan
Published: September 2020

In order to integrate different kinds of information in the water affairs field, this paper proposes an improved comprehensive semantic similarity algorithm and applies it to the construction of water affairs ontology. In this article, the algorithm is to give the concept instance similarity algorithm and concept definition similarity algorithm appropriate weight. First, we apply the three algorithms to the concept of the local ontology of smart water, and then calculate the similarity between the concepts to judge the similarity between the concepts to merge the smart water data, and finally compare the similarities of the three algorithms Degree value and calculation and comparison of three similarity variance values prove the effectiveness of the method. It can be seen from the experimental results that the variance of the comprehensive similarity calculation method is smaller than that of the other two methods, and it is easier to determine the similarity between two concepts.
A Multi-UAV Formation Maintaining Method Based on Formation Reference Point
Authors: Jiang Yang , Wang Shunzhang , Lei Lei
Published: September 2020

In recent years, Unmanned Aerial Vehicles (UAV), have been increasingly utilized by scholars. Efficient control of UAV swarms opens a set of new challenges. There are several formation control methods, however, artificial potential field (APF) method has imperfections in formation maintaining and virtual structure is insufficient in collision avoidance. In this paper, we proposed a multi-UAV formation maintaining method combined artificial potential field method and virtual structure approach. The method uses the concepts of attraction and repulsion in the artificial potential field method, combined with formation reference points inspired by the virtual structure points in the virtual structure approach to control the formation. On the other hand, we express the leaders, the followers, and the formation reference points uniformly. When the formation of UAVs is determined, after the leader arrives at target point and hovers, the followers can track the formation reference points. The method can avoid collision between UAVs and achieve the purpose of formation maintaining. Finally, a UAV swarm consists of four UAVs is used for demonstration and verification, and the results of experiment prove the validity of the algorithm.
Robot Path Planning Method Based on Level Set
Authors: Wang Ting , Sui Jianghua
Published: September 2020

For the problem of robot path planning under the action of external force field, the directionality of outward force can promote and hinder the movement of the robot. The continuous path planning method can break through the traditional robot path planning algorithm based on grid search. Limit, effectively utilize the external force field existing in the space, and improve the numerical implementation method to ensure the feasibility of the planned path in the strong external force field. Based on the Level Set method, the path planning problem is defined. The particle tracking of the robot is transformed into the numerical evolution of the curve. The optimal time path is obtained by solving the Hamilton-Jacques equation. The numerical calculation accuracy of the improved backward path tracking is guaranteed. The feasibility of planning the path. The simulation results show that the algorithm can effectively deal with a variety of complex external force fields and maintain the path in a strong external force field.
Study on RLS-Type Algorithm of GPS-Guided Range Correction
Authors: Chen Jun , Du Zhonghua , Shen Jian
Published: September 2020

Mitigating the error of GPS measurement signals is essential for the range precision of GPS-guided range correction projectile. In order to improve the range precision, an online method based on recursive least square algorithm (RLS) is applied to calculate the deployment time of brake. First, RLS is utilized to mitigate the error of the velocity, position, angle, etc., measured by GPS. Then, the deployment time can be calculated accurately by means of the appropriate pretreated data. To confirm the effectiveness of the method, a comparison to using least square algorithm and non-algorithm is done. The experimental results demonstrate that the method in this paper takes advantages in terms of online, real-time and the range correction accuracy.
Security Analysis and Optimization of BB84 QKD System Post-Processing
Authors: Zhang Pin , Mao Xiangjie
Published: September 2020

Considering the rapid development of quantum technology, it has become inevitable to use this technology to develop an ultimately secured key exchange system. Quantum key distribution (QKD) technology has become more sophisticated, is expected to provide secure key distribution mechanism for the actual information security systems. The unique advantage of quantum cryptography is precisely its ability to guarantee absolute security. Previous works have demonstrated unconditional security of the ideal BB84 QKD system. However, on a typical QKD system, many practical situations often cannot satisfy the requirements of the ideal condition. This problem causes some security issues for practical QKD system. Therefore, it is necessary to analyze the security of QKD system under practical conditions, and identify the security implications on the practical physical systems and equipment, in order to find a method that guarantees the security of QKD system. The security of QKD system based on BB84 involves many aspects; this paper mainly focuses on how to resolve security issues that may exist, especially in the BB84 protocol post-processing. We introduce ways to tackle these issues and make vital key information leakage very small. Their impact on the security of entire QKD system is analyzed, and the interaction and security optimization between the various parts is researched. Finally, the thesis briefly introduces five sub-modules of post-processing of QKD system: the base comparison, error estimation, information correction, data check and privacy amplification; the existent security factors of each module is given, for error estimation and information correction is provided the secure model with those issues. Thus, the security model of post-processing is further optimized.
A Complete Adaptive Method for Fetal ECG Extraction Based on Single Channel
Authors: Zhang Miao , Wei Guo
Published: September 2020

Based on empirical mode decomposition (EMD) and coherent averaging method, a completely adaptive single channel fetal ECG (FECG) extraction method is proposed. EMD is used to decompose single-lead abdominal ECG (AECG) signals, so that the noise, FECG and MECG components main energy in AECG are distributed in different inherent mode functions (IMF), and R wave detection of MECG is performed by means of IMF, where MECG energy is dominant. MECG was extracted from AECG by means of coherent averaging based on R wave detection results. The remaining signal was named as noisy FECG. Usually, when MECG residue in noisy FECG is large, it needs to be further eliminated. To this end, the same method as above was used again to extract FECG from noisy FECG, and threshold method was used to eliminate MECG residue after extraction of FECG, and then superimposed onto FECG again, in order to recover the non-stationary feature lost in the coherent average process. The effectiveness of the proposed method is verified by the experiments of synthetic mixed signals and real database recorded signals.
Research on Recognition Algorithm of Abnormal Behavior of Workers in Two-Stream Convolutional Network
Authors: Zhao Yinong , Li Ruoxi , Zhang Xuedong , Cao Yuhan , Chen Xiaojing
Published: September 2020

In order to identify the abnormal behaviors of workers in the factory, this paper proposes an improved algorithm for identifying abnormal behaviors of workers in a two-stream convolutional network. The workers’ body contour shape information extracted from the convolutional neural network is input into the LSTM network in order to extract timing information between frames. Secondary extraction of the dense optical flow image of the video image, sparse extraction of pixels with small changes in optical flow value in the dense optical flow image, and then put the new continuous optical flow image into the continuous optical flow image network. The two networks are fused after softmax classification to get the final recognition result. Experiments on the CAVIAR dataset, CASIA dataset, and self-built behavior dataset show that compared with other abnormal behavior detection methods and traditional two-stream convolution algorithms, the accuracy of the improved algorithm in this paper is improved by 1% -4%.
Attitude Regulation for Multiple Spacecrafts Based on the Distributed Fixed-Time Observer
Authors: Zhao Lin , Zhao Kun , Huang Weiquan
Published: September 2020

This paper has investigated the distributed attitude regulation problem for multiple spacecrafts on the basis of the leader-follower architecture. In this paper, a control scheme based on the distributed fixed-time observer is proposed. To be specific, with the condition that some following spacecraft can’t obtain the leader’s attitude, a distributed attitude observer is introduced for estimating the leader’s attitude. By using the distributed observer, this issue will be transformed into an attitude tracking control problem for a single spacecraft. It is proved that the proposed distributed observer is in the sense of the fixed-time stability. Finally, simulation results illustrate that the proposed distributed observer is effective.
FPGA Accelerator Design for License Plate Recognition Based on 1BIT Convolutional Neural Network
Authors: Cai Yulu , Lin Xiunan , Qian Hui , Lu Peimin
Published: September 2020

Aiming at the problem that convolutional neural network is difficult to deploy on small embedded devices due to its high complexity and large storage space requirement, this paper propose a convolutional neural network FPGA accelerator architecture based on binarization. Using the gray scale processing, binarization processing, threshold setting to reduce the number of parameters. Designing Parallel structures of convolution kernels, feature maps, and matrix blocks to accelerate. The designed architecture can be deployed on the AX7103 FPGA development platform with limited resources. The experimental results show that the convolutional neural network after parallel acceleration design can achieve a recognition accuracy rate of 98.73% on the premise of reducing the data bit width from 32 bits to 8 bits, the recognition speed is about 0.21 seconds/time.
Dynamic Fuzzy (λ,η)–Function Rough Sets
Authors: Xie Weihe , Fan Chuanqiang , Liu Feng
Published: September 2020

By learning function S-rough sets, we find that transfer functions play the roles in practice are that define which elements into the set Q and which elements left the set Q. But the changed elements have been mapped into what kinds of elements that are not very important, so it is not necessary to intensive study the transfer functions. Thus we can defines a dynamic fuzzy sets in the set D to reconsider the fates of the elements of Q. By using function S-rough sets and dynamic fuzzy sets, the new definition of dynamic fuzzy (λ,η)–function rough sets are given, then the mathematical structure and characteristics of dynamic fuzzy (λ,η)–function rough sets are discussed. The structural relations of dynamic fuzzy (λ,η)–function rough sets and function S-rough sets are deeply deliberated. Dynamic fuzzy (λ,η)–function rough sets are the general form of function S-rough sets, then function S-rough sets are the special cases of dynamic fuzzy (λ,η)–function rough sets. Dynamic fuzzy (λ,η)–function rough sets are the new direction in the study of rough sets. In terms of practical application, dynamic fuzzy (λ,η)–function rough sets can achieve some better results than the other methods.
Anomalous Sound Detection Based on Convolutional Neural Network and Mixed Features
Authors: Zhao Jie
Published: September 2020

In this paper, convolutional neural network (CNN) is used to detect abnormal sound. Aiming at the problem that the audio time-frequency graph contains complex feature information and the single traditional audio feature contains insufficient information, a new feature graph is proposed, which can be used as the input of the CNN model to get more accurate detection results. Firstly, the two-dimensional time-frequency graph is obtained by the short-time Fourier transform of the abnormal sound signal. Secondly, this paper extracts audio features such as MFCC and short-term energy from the time-frequency map and the original audio. Finally, this paper uses matrix transformation to combine the features and takes the processed feature graph as the input of the CNN model. And the CNN model was trained to judge the category of abnormal sound. The results of measured data show that the combination of CNN and mixed features can accurately judge and classify abnormal sounds.
Optimization of Chlorine Bleaching Parameters for Indigo Denim Textile Based on the Model of Response Surface Model and Genetic Algorithm
Authors: Li Sheng , Fang Xuejiao , Zhou Xinyi , Bie Bihan , Xu Jie , Yi Changhai
Published: September 2020

The production of denim clothing has been broadly applied with the sodium hypochlorite washing technique, the parameters of which influence immensely on the effect of washing. It is the unclear declaration of the correlations between parameters of this technique and the final effects that makes it difficult to reduce the cost of washing in an efficient way. In this essay, L25(35) Orthogonal experimental design method has been adopted to process chlorine bleaching experiment, by using parameters of the technique as the input parameters ( the time, sodium hypochlorite concentration, temperature), and by using after-washing effects as output response ( including K/S value, the bending stiffness, the warp-weft tensile breaking strength). Secondly, building agency theory model for the input-output correlation by utilizing response surface methodology in multivariate second-order to test the accuracy of the model, and to analyze how input parameters correlate to the result of chlorine bleaching. At last, based on the agency model, there will be raised up with the optimization model for production cost of the denim chlorine bleaching technique, which is solved by genetic algorithm method that could satisfy the purpose of reducing costs.
Comparison of Gradient Descent and Least Squares Algorithms in Deep Model
Authors: Jin Lianchao , Jiang Shengming
Published: September 2020

Deep neural networks have a wide range of applications in stock forecasting, big data forecasting of influenza outbreaks, prediction of game outcomes and so on. They all involve a common problem—regression prediction problems. Deep model can be applied to solve regression prediction problems by optimizing model structure through optimization algorithm. The optimization algorithm generally uses the gradient descent method to optimize the model structure by inputting much real data. When the previous mathematical model solves the problem of regression analysis, one of the most basic optimization methods is the least squares method. This paper studies the performance of the vgg16 convolutional neural network model combined with transfer learning reconstruction when the amount of data is large. By analyzing their algorithm structure to more clearly understand how they are optimized for deep model, better algorithms can be used for future model optimization.
Research on Influencing Factors of On-Street Parking Based on Empirical Mode Decomposition
Authors: Wen Haoyu , Zhang Yuxue , Wu Zhipeng
Published: September 2020

The change in the quantity of on-street parking needs more attention, and it changes with factors such as seasonality and holidays, by which both direct and indirect influence may be caused. Hence, in this paper, problems of how to analyze the quantity sequence of on-street parking through Empirical Mode Decomposition (EMD) are focused due to the quantity of on-street parking properties that can be labelled as nonlinearity and non-stationarity. By analyzing parking data in Xi’an, using EMD to decompose the quantity of on-street parking, analyze the influencing factors in each mode, and analyze the factors that affect on-street parking from a quantitative and qualitative perspective. Therefore, the effects of high frequencies (such as holidays) and low frequencies (such as seasonal) can be obtained. Our work will be reasonable for policymakers and business supervisors who wish to plan the on-street parking berths more reasonably.
Numerical Simulation on the Production of Glass Fiber Filter Product and Uniformity Improved on Cross-Direction
Authors: Zou Chi , Qian Xiaoming , Zhang Lingyun
Published: September 2020

Glass fiber felt is produced by flame blowing process, but in the traditional flame blowing process, the uniform of product is affected by the airflow. When glass fiber through the air flow jet into the diffuser duct and reach to the web curtain, it is difficult to diffuse the width of the product needs. Researches show that, the size and shape of diffuser duct is critical to the movement of the glass fiber suspension in the duct, eventually the width and density distribution at the web curtain. In this paper, numerical analysis with experiment-based boundary conditions were used to the redesigned diffuser duct. The redesigned duct was built and produced, the product uniformity on cross-direction was tested and it has a good agreement with simulation analysis results. Through the redesign of the diffuser duct, improved the fabric weigh of the product in the cross-direction, get a more uniform product.
Study on Spontaneous Behavior Recognition of Mice Based on Frame Stream and Feature Coordinate Matching
Authors: Chen Biao , Zhuang Yuan , Xu Gang
Published: September 2020

The recognition of spontaneous behavior in mice is of great significance to the biological research. It not only provides an important means for pathology, pharmacy, biological neurology, but also provides great convenience to scientific researchers. In this paper, the monitoring video of mice were taken as the research object. Based on Classical frame average method to background modeling and PBAS algorithm solved the whole detection and tracking of mice, and detection and tracking of local characteristics in mice were solved by residual neutral network (ResNet). On this basis, the spontaneous behavior recognition of mice was solved by K-Means clustering algorithm. To improve the accuracy, we proposed a method of spontaneous behavior recognition in mice based on frame stream and feature coordinate matching. The effect of recognition was intuitionistic and obvious, and met the needs of subsequent experiments on matching with large-scale neuronal spike sorting in mice.
Research on Selective Strategy of Enterprise Network Marketing Channel—Take Leaf Tableware as an Example
Authors: Ouyang Mulan , Li Yao
Published: September 2020

With the continuous development of Internet information technology and the improvement of market economy, the competition among enterprises is becoming more and more fierce. Therefore, it is the primary task for major enterprises to find and perfect effective marketing strategies. With the rapid development of network information technology, more diversified network marketing channels have been created for the marketing work of enterprises. Therefore, through the correct combination of the relevant factors of market environment, enterprise resource conditions and development status, choosing the network marketing channel suitable for the survival and development of enterprises can become a favorable competitive advantage for enterprises to occupy the market. On the basis of combining the network marketing channel design of leaf tableware, summarizing and analyzing the current situation of enterprise network marketing channel, this paper is aiming to help the enterprise select a reasonable and effective network marketing channel and ensure the smooth implementation of the marketing strategy through decision tree and CART algorithm.
Research on Networked Hydraulic Synchronous Control System
Authors: Lu Feiping , Xue Wen , Zhu Xuhua
Published: September 2020

Networked synchronous control systems (NSCS) are developed rapidly. Networked synchronous control system refers to the distributed subsystems whose outputs need to be synchronized are connected through the network to form a distributed control type, and multi-node coordinated synchronization. Subsystems of hydraulic synchronous systems are normally distributed in a wide range of industrial area. The requirement of low-cost synchronous inter-subsystem data collection, communication and system distribution control are achieved by applying internet connection. In order to transmit sensing and control information, each subsystem inevitably generate network delay issues for networking synchronous control system. Therefore, setting up a hydraulic synchronization control system error model with network delay function to solve the network delay problem. Meanwhile, the robust asymptotic synchronization controller is designed by robust control theory, which could adopt a series of relevant simulations and experiments to verify the effectiveness. It has great engineering application value.
Research on the Application of Competency Model in Personnel Management of Private Enterprises
Authors: Gong Xiaopeng , Liu Ruijie
Published: September 2020

With the continuous deepening of China’s social development and market-oriented, China’s private enterprises have flourished and achieved unprecedented breakthroughs. The private economy has become an important force and source for promoting national economic growth. At the same time, with the continuous development of the trend of economic integration on a global scale, the market competition faced by private enterprises has become increasingly fierce. This article uses the behavioral event interviews, from the six modules of human resource planning, recruitment and configuration, training and development, performance management, compensation and benefits management, employee relationship management to explore the application of competency model in private human resource management system, and to provide suggestions for talent management of different types of private enterprises.
Research on the Real-Time Mechatronic System Simulation Based on Virtual Reality
Authors: Zhao Yan
Published: September 2020

Large-scale equipment has the characteristics of complicated structure with the hazardous working conditions such as high temperature, high pressure, high voltage and other extreme and dangerous environment, which is difficult for operators to master the use of this kind of equipment in the field with short time. The virtual reality technology provides the possibility for operators to receive remote virtual training in a safe place, and has been the hotspots recently. In this paper, a mechatronic system simulation method is proposed for operators to master the use of large-scale mechatronic equipment with the help of virtual reality technology. The real-time simulation of virtual mechatronic system is achieved by the task scheduling, fault injection, mechatronic system and load simulation, where the Levels of Detail (LOD) technology, parallel simulation technology, distributed simulation technology and batch processing technology are used to further optimize the system scheduling time. With the combination of multi-channel video images and dynamic video adjustment, a suitable way to construct the display screen can be selected, and the operator can complete the training with a better perspective, which achieves the purpose of virtual training. Finally, the example of a mechatronic system simulation interface is implemented to verify the effectiveness of the simulation method.
Main Steam Temperature Prediction Modeling Based on Autoencoder and GRU
Authors: Zheng Ling , Cao Xinwei , Chen Fei
Published: September 2020

There is an important parameter in the normal operation of thermal power plant units, that is, the main steam temperature, too high or too low will have a great impact on production. For safety and efficiency considerations, the main steam temperature needs to be controlled within a stable and appropriate range. In this way, the safety of equipment and people is guaranteed, but also ensures that resources will not be wasted. The prediction of the main steam temperature can achieve this aim. In view of the numerous influencing factors and non-linear characteristics of the main steam temperature, this paper proposes a main steam temperature prediction model based on autoencoder and GRU. First, build an autoencoder model, use historical data for training, enable it to accurately extract the essential information of the data. Then multiple influence parameters are dimensional reduced by the trained autoencoder, and use the reduced data as the input of the neural network built using GRU for training. The better model is finally compared with RNN and LSTM to verify the effectiveness and accuracy of the method.
Image Super-Resolution Restoration Instance-Based Learning and Iterative Kernel Regression
Authors: Huang Juxiu , Feng Lei , Liao Jingxing
Published: September 2020

This paper proposes an image super-resolution restoration algorithm based on example learning and iterative directional kernel regression, which is used to solve the problem that the existing super-resolution restoration algorithm based on example learning cannot effect the restored image has a small degree of matching with the sample library and the problem of image restoration in the presence of noise. Among them, the example learning can achieve basic image restoration. In the directional kernel regression, the estimated smoothing matrix can obtain the minimum mean square estimation after multiple iterations, and further optimize the super-resolution restored image. Simulation results show that the improved algorithm improves the robustness and edge preservation characteristics of the super-resolution restoration image. Compared with the classical algorithm, the algorithm has better visual effects and the root mean square error can be improved over 15%, and reflect the effectiveness of the algorithm.
A Design of Humanoid Foot Control System
Authors: Zhang Chuqi , Du Qiaoling , Ni Boyang , Chen Chufan
Published: September 2020

Humanoid robots can be used in high-risk operations or to replace human repetitive tasks. The basis of its various subsequent functions is the stability of biped walking. A humanoid foot structure which adapts to the ground condition and changes the contact mode is designed. The mechanical structure of robot foot is divided into two parts: front sole and rear sole. Using flexibility force transducer array and temperature transducer to monitor landform and ground data. The mechanical structure of this humanoid robot foot is such that the robot can realize passive deformation of the foot. The active deformation of the robot foot is realized by STM32 controlling servo. The structure contains four kinds of working modes, can make the robot achieve semi-passive walking, with certain robustness. In this paper, we design the mechanical structure of the robot foot and its control method. Finally, the feasibility of the foot structure is verified by experiments.
Dynamic Modeling and Cooperative Process Simulation in Cooperative Dual-Arm Robot Based on Adams
Authors: Wang Jian , Zhou Lili , Chen Lei , Li Yuanyuan
Published: September 2020

The demand of cooperation task of cooperative dual-arm robot in industrial automation production is increasing day by day. It is analyzed that the kinematics and dynamics mathematical model of the cooperative dual-arm robot with six degrees of freedom in one arm and the relationship is constructed between the arms’ position and the attitude for the robot in the paper. The mechanical characteristics of the cooperative tasks are analyzed that contains carrying, assembling and processing in the industrial field. The dynamic simulation software Adams is used for the simulation calculation and analysis. It is obtained that the position and attitude transformation characteristics of the forward kinematics solution through the simulation analysis, and the curve of the distribution force for the dual-arm in the process of cooperation is obtained. It provides the basis for setting up the reasonable driving force distribution and load distribution when realizing the task for dual-arm robot in cooperation.
Segments-Based 3D ConvNet for Action Recognition
Authors: Li Wei , Xu Ning , Liu Ge , Zhao Linglan , Fang Xiangzhong
Published: September 2020

Learning to capture both long-range and short-range temporal information is crucial for action recognition task. Previous works utilize 3D ConvNets to capture short-range temporal dynamics in replacement of optical-flow which needs time-consuming extraction. However, dramatically incresed parameters limit the capacity for modeling long-term interactions. In this paper, we propose Segments-based 3D ConvNet (S3D) to integrate both long-term and short-term temporal dynamics. Firstly, we utilize 3D ResNet without temporal downsampling to capture short-range video contents. Secondly, we integrate a sparse sampling strategy to model long-range temporal structure. Finally, experiments on UCF-101 and HMDB-51 datasets show the effectiveness of our S3D compared with corresponding 3D ConvNet.
The PBD Model Based Simulation for Soft Tissue Deformation in Virtual Surgery
Authors: Liu Yanan , Guan Chengbin , Li Jianhui , Yu Xueli , Zhang Shuang
Published: September 2020

With the development of computer technology, surgical simulators based on virtual reality have been widely studied, and the key technology of virtual surgery simulation is deformation. The simulation of soft tissue deformation mainly includes two aspects: (1) Using the appropriate physical model for deformation calculation; (2) The virtual surgery simulation has collision detection, force feedback and so on. In the design of deformation algorithm, the above two problems should be considered at the same time to ensure that the system has high real-time and authenticity. Aiming at the above problems, this paper used the method of position based dynamics (PBD), which can effectively simulate the effect of soft tissue deformation.
Application of an Improved Hough Transform and Image Correction Algorithm in ACC
Authors: Chen Biao , Ding Bangfeng , Wang Jiangtao
Published: September 2020

Numerous studies have shown that ACC (Augmentative and Alternative Communication), as a tool in language and communication training for children with autism, has effectively improved and played a positive role. ACC’s Picture Exchange System (PECS) provides an important support for the correction and feature recognition of language and training teaching cards. When designing the picture exchange function of ACC, this paper proposes a modified Hough transform and image correction algorithm suitable for teaching cards to solve the problems of large data calculation, time-consuming calculation, large space occupation and false peaks caused by traditional Hough transform algorithm in detecting straight lines, which also optimizes the interactive experience of the teaching scene. Finally, we design two experiments to compare and analyse the classic Hough transform, random Hough transform and improved Hough transform. Experimental results show that the improved Hough transform algorithm has superior performance in terms of execution efficiency and accuracy of line detection.
Research on Sound Source Localization and Real-Time Facial Expression Recognition for Security Robot
Authors: Han Zhonghua , Li Tingting
Published: September 2020

Security robots are intelligent equipment used to perform security tasks, which can play a role in reducing the labor intensity and duty risk of police. In view of the shortcomings of existing police security robots in human-computer interaction and cooperation, the sound source localization and real-time facial expression recognition for the security robot is introduced in this paper. The auditory system of the robot obtains external sound analog signals through a microphone array, and performs corresponding digital signal processing on the collected sound signals. In order to strengthen the information interaction capability between the security robot and operators, the Kinect with deep data processing capabilities is introduced in this paper. The experimental results show that the system can analyze and recognize the position and status of the operators.
Quaternion Mahalanobis Non-local Means for Color Image Denoising
Authors: Xu Xudong , Zhao Jinling
Published: September 2020

By improving the quaternion non-local means (QNLM) image denoising algorithm, the quaternion Mahalanobis non-local means (QMNLM) method is proposed. This method combines quaternion with non-local similarity priors and introduces quaternion Mahalanobis distance. Firstly, the Mahalanobis distance between the image patches is calculated in the eigenspace by singular value decomposition of quaternion since the Mahalanobis distance is not robust in the sample space. Secondly, the image data is analyzed with the principal component analysis method, thus the Mahalanobis distance equation is simplified. The image with noise affected by high noise was first processed with a Gaussian low pass filter (LPF). Experimental results show that the noise reduction effect of this method is better than the non-local means (NLM), generalized non-local Means (GNLM) and QNLM methods. The proposed algorithm can effectively eliminate image noise and efficiently improve the image visual effect.
Using Graph Convolutional Networks Skeleton-Based Pedestrian Intention Estimation Models for Trajectory Prediction
Authors: Cao Dong , Fu Yunbin
Published: September 2020

In autonomous driving scenarios, pedestrian trajectory prediction is an important research direction. Based on the spatio-temporal graph convolutional neural network, we propose a new pedestrian trajectory prediction algorithm. The new algorithm constructs a series of new models around pedestrian intention estimation. The construction of the estimation algorithm considers the following aspects: the contextual information of pedestrians and the surrounding environment, the “pedestrian ego-vehicle” interaction combined with the vehicle speed estimation, the pedestrian’s own skeletal structure information and body language estimation, which includes head joints and the relative structural relationship of the torso joints, including whether it is out of the same plane, is rotated, and so on. Skeleton information feature extraction and construction adopts the method of graph convolutional neural network to structure pedestrians into joints in the form of graphs in non-Euclidean space, and further adopts spatial temporal graph convolutional network for feature extraction and learning. The new method is named a “head-torso”-based spatial temporal graph convolutional network (HT-STGCN). On the dataset PID, the novel method achieves substantial improvements over mainstream methods. Experimental results show that combining HT-STGCN with observed action can improve trajectory prediction.
Research on Vehicle Tracking Algorithm Based on Deep Learning
Authors: Zhou Yan , Zhou Jun , Liao Fangli
Published: September 2020

Comparing with the advantages and disadvantages of the existing target tracking algorithms based on deep learning, a vehicle tracking algorithm based on Yolov2 and GOTURN algorithm is proposed, which is called YOLOv2-tracker vehicle tracking algorithm. The Algorithm is trained and tested by using the collected training set and test set. The results show that the YOLOv2-tracker vehicle tracking algorithm can achieve higher tracking accuracy and faster tracking speed, and can effectively overcome environmental interference. Further analysis of the test results, the algorithm found that there is “errof” phenomenon, the paper discusses and analyzes the causes of this phenomenon, and put forward a reasonable solution. In addition, a “dynamic save” method is proposed to solve the “lost track” problem.
Design and Implementation of English Text Recognition System under Robot Vision
Authors: Wang Gang
Published: September 2020

The traditional methods of English letter visual recognition are machine learning and contour feature extraction. However, limited by the hardware resources such as camera and processor, it is difficult for the robot to realize fast and accurate text extraction and recognition. By comparing the results of domain average method, median filtering method and KNNF filtering method in text image preprocessing of robot, this paper proposes to use median filtering method for image preprocessing and corner detection method for text area extraction, which improves the efficiency of text image processing for robot. Based on the writing characteristics of English letters, a concentric rectangle tangent based on equal difference area is proposed The overall structure of the algorithm solves the problem of fast recognition of multi-directional mixed text, effectively reduces the influence of the angle between robot vision and pre recognition target on text recognition, and improves the speed and accuracy of text recognition by optimizing SOFM network. The system model has been verified based on Nao robot platform.
An Empirical Study on Utilizing Neural Network for Event Information Retrieval
Authors: Li Minglei , Zhu Yu , Wang Ren
Published: September 2020

Event information retrieval (EIR) is the task of retrieving news articles in response to the event-oriented query, which tailors for dealing with a large scale of news articles on breaking news (e.g., natural disaster). However, the challenges caused by the dynamic and complex natures of the event make EIR less studied than traditional information retrieval (IR). The existing studies usually employed either traditional IR models or machine learning approaches over hand-crafted features to retrieve events, which fails to capture more-complex structures of events with event evolution well. To address this issue, we exploit neural models incorporating the characteristics of events to EIR task. Experimental results demonstrate the effectiveness of our method over two datasets, which can regard as the simple but strong baseline for further research in EIR.
Tracking Control for a Rehabilitative Training Walker Considering Human-Robot Interaction
Authors: Sun Ping , Sun Yao
Published: September 2020

A new extended state observer combined with a Backstepping tracking control method were proposed in this paper for the omnidirectional rehabilitative training walker. The aim of this study is to obtain a stable tracking controller, and it considered the interaction forces of the user and walker to ensure that the omnidirectional walker can track accurately specified trajectory. The extended state observer was designed to estimate the unknown interaction forces of user and the walker using output position and speed states. Further, a Backstepping controller based on the observed value was constructed to resist the adverse effects of interaction forces. The asymptotic stability of the trajectory tracking error, the velocity tracking error and the state observation error were guaranteed. Through programming, simulation results show the effectiveness of the proposed design method.
Research on Improved Triangular Mesh Surface Subdivision Algorithm
Authors: Ma Hua , Zhu Yanan , Niu Xinya , Wang Wenshuai
Published: September 2020

Subdivision surfaces have been widely used in computer graphics. They are also commonly used to subdivide surfaces in 3D geological modeling. The classic triangular mesh subdivision algorithms are Loop subdivision algorithm, Sqrt3 subdivision algorithm and Butterfly subdivision algorithm. Based on the realization of the three algorithms, the paper displays that the improved Butterfly subdivision algorithm, compared with the Sqrt3 subdivision algorithm and the Loop subdivision algorithm, can alleviate the contradiction between data volume and smoothness. It can also achieve a better fittness, and can deal with the smooth subdivision of the model well in 3D geological modeling and other applications.
Study on BP Neural Network Optimization by Improved Decay Parameter Genetic Algorithm
Authors: Jiang Yi , Zhang Fan
Published: September 2020

BP neural network optimized by standard genetic algorithm is hard to meet the balance between global search capability with local search capability. Therefore, the decay parameters genetic algorithms were presented to improve the convergence rate and accuracy. The results of this approach were compared with that of the standard genetic algorithm as well as the improved genetic algorithm by MATLAB simulation software. The simulation results show that the decay parameter genetic algorithm has better accuracy and robustness than the standard genetic algorithm, and greatly gives consideration to both the global search ability and the ability of local search. As a result, the precision and speed on the system fault diagnosis are dramatically improved.
Deep Metric Learning Based Histopathological Image Classification
Authors: Zhang Gang , Xu Hui
Published: September 2020

With the rapid development of deep learning, histopathological image classification models have made remarkable progress. Recent deep learning-based methods have been applied to raw histopathological images to construct end-to-end models, which avoid hand-craft feature engineering. To construct a model that can capture the intrinsic pattern of the histopathological image dataset, we design a model based on deep metric learning which embeds data points into a Euclidean space. The proposed model trains a deep neural network, which embeds an input image into a Euclidean space where dissimilar images are located far away to each other and vice versa. We adopt a BN-Inception network pretrained on ImageNet as the embedding model. Then it is retrained on target datasets with some triplet loss function. A weighted distance-based triplet sampling strategy is designed to generate hard triplets for the training procedure. Evaluations on benchmark datasets indicate that our deep metric learning-based method outperforms recent successful deep learning models.
Construction and Application of Field Investigation Support Platform for Land Spatial Planning Based on GeoServer
Authors: Dai Yao , Duan Zengqiang , Ai Dong
Published: September 2020

Basic data and satellite images, such as topography, soil, cover type, social economy and so on, are very important for planning, and many of the data are scattered among multiple Internet resources and researchers. At present, geographic information system and Internet technology are developing rapidly. Many institutions have published a large number of thematic maps related to resources and environment on the Internet, and can realize the aggregation of these Internet maps in GIS software through WMF, WMTS and other protocols. Relying on the rapid development of cloud virtual server in recent years, the technology and economic feasibility of deploying WebGIS server to distribute proprietary data thematic map are greatly improved. With the help of cloud server, this paper constructed WebGIS service with the assistance of GeoServer and published thematic map information on OpenLayers as the client, so as to provide basic information related to natural environment and social economy for territorial space planning.
Research on Mechanical Deviation Correction Method for Industrial CT
Authors: Liu Jiayin , Yang Long , Wu Yuan
Published: September 2020

Industrial CT is the abbreviation of industrial computed tomography (CT) technology, which adopts X-ray imaging principle to realize the non-destructive three-dimensional imaging to obtain high-precision three-dimensional tomographic data and volume data inside the product, clear, accurate and intuitive. In addition, it can show the internal structure, composition, material and defective condition of the inspected object. Factors such as mechanical accuracy and mounting deviations can adversely affect CT imaging and, in severe cases, lead to CT imaging failure. It is important to achieve effective correction based on the existing CT mechanical accuracy and mounting deviations. In this paper the authors collect data and performed experiments in a real CT system, they use standard test blocks to correct the deviations while keeping the existing CT system unchanged, the effectiveness can be validated by the experimental data.
Dynamic Chain Analysis by Bipartite Network for Medicine Selection
Authors: Yin Xinming , Guo Yi , Cao Zhiwei , Xiong Min
Published: September 2020

The rapid development of society has brought about the uncertainty of social relations, and the structure of social networks is constantly changing. Chain forecast, as an effective method, plays an increasingly important role in walks of life in understanding the dynamic nature of the network and determining future relationships, combining the structural characteristics of the present status of the network to foresee the possible existence of future network nodes, this paper proposes a Chain forecast method for Disease treatment and a Chain forecast method based on bipartite networks (such as treatment correspondent diagrams). In order to verify the forecast effect of the method, we selected several Chain forecast algorithms for check. The results prove that our proposed method is better than other methods based on Chain forecast.
Sonar Image Translation Using End to End Network for Underwater Recognition
Authors: Hua Jin , Liu Mengzhao , Xin Daxin , Liu Yixin
Published: September 2020

With the rapid development of sonar technology, the classification of underwater sonar images is of great significance. In order to recognize underwater sonar images more effectively, side scan sonar is used for data acquisition. After pre-processing, six types of sonar images are selected. Combining with the characteristics of underwater sonar images, relevant theories and methods of convolutional neural network are studied to establish an underwater sonar CNN recognition model, which achieves 98.5% recognition success rate. Experiments show that the recognition model can improve the accuracy, speed and robustness of underwater sonar image classification, and can meet the practical application requirements.