bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.