bluuebunny/crossref_metadata_2025_split huggingface dataset:
Vision-based robot identification and tracking
Authors: He Dong , Lu Jun , Zhou Qinghe , Yin Hongjie 
Published: July 2021

Abstract
In recent years, computer vision technology in the field of science and technology has been stubbornly high, making a lot of scientific and technical personnel involved in the research gate technology. Depth learning, artificial intelligence in recent years, often around our ears, vision technology is occupying a place in the field of artificial intelligence. This paper mainly studies the recognition and localization of robots based on vision. The mobile robot is detected by the background difference method, and the background model is established by the Gaussian background modeling. Recognizing the color and number tags on the robot for matching the robot’s features. Real-time positioning of the robot under the premise of the same background.
Influence and analysis of atmospheric attenuation on the performance of virtual lidar
Authors: Cheng Xu , Zhang Dongze , Li Xu , Li Xiaoting , Chen Rongjie 
Published: July 2021

Abstract
The complexity of research and development process and the diversity of research methods of intelligent connected vehicles increase the difficulty of establishing, integrating and verifying of virtual simulation experiments. In order to solve the problem of how to quantify the impact of weather environment changes on the virtual lidar in the simulation experiment, this paper first uses the derivation of the lidar range model and the analysis of the influence of atmospheric attenuation on the lidar range. Then, with the help of the empirical model of the attenuation coefficient of rain, snow, and haze, the curve fitting method is used to simplify the influence relationship so that it can be applied to the virtual simulation software of autonomous driving. Finally, the scene verification and evaluation of the quantification method of the lidar in the virtual environment affected by weather changes are carried out. The results show that this method effectively improves the restoration degree of the virtual simulation sensor while ensuring the calculation efficiency.
Research on the release management technology of the mobile application for electricity dispatching
Authors: Dai Jinxia , Li Donghai , Gao Baoshan , Zhou Ling , Chen Wenjing 
Published: July 2021

Abstract
With the continuous development of the power system and the rapid growth of the scale of the power grid, the information that needs to be monitored, inquired, and maintained in the power grid dispatching system is increasing. The traditional way of understanding the realtime operation status of the power grid through dispatching workstations is increasingly unable to meet the requirements of modern power grids for monitoring, operation and maintenance in real-time, timeliness, and anywhere. In the face of such a huge business demand market, how to develop mobile applications that meet customer needs with minimal investment in resources and efficient development efficiency has become our research direction. This paper discusses a release management technology of the mobile application for electricity dispatching that meets the above requirements. This technology adopts the design mode of “shared application + personalized page”, without too much intervention by developers, Users can edit and compress their own personalized pages, then upload to the version server and update the main page on the mobile devices themselves; this technology improves the efficiency of opening graphics file and the response time of operation through caching the graphic files and only refreshing the operation area mechanisms; through the publish/subscribe message bus mechanism, the hot update effect of the main page is realized; the service is restored through one-click, which provides users with more operating space.
Research on automatic robot charging based on infrared and ultrasonic information fusion
Authors: Hongjie Yin , Jun Lu , Qinghe Zhou , Dong He 
Published: July 2021

Abstract
In order to realize long-term, continuous, stable and reliable operation of wheeled robot without intervention, the principle of autonomous charging technology is analyzed, and the common and key technologies of wheeled robot autonomous charging are introduced. Aiming at the disadvantages of traditional autonomous charging system. A robot automatic charging docking method based on the combination of infrared radiation sensor, photoelectric tube tracking sensor and ultrasonic distance sensor is adopted. through the data fusion of Kalman filter algorithm for the distance value obtained by ultrasonic sensor, a high precision distance estimation value is obtained, which makes up for the lack of measurement accuracy in traditional infrared method. Experimental tests show that the docking accuracy between robot and charging seat is high. The docking is completed well.
Parallel simulation method of GNSS multipath signal based on GPU
Authors: Kang Xin , Wang Rui , Lv Zhicheng , Li Baiyu , Mou Weihua 
Published: July 2021

Abstract
In order to solve the problems of the traditional GNSS multipath signal simulation methods, such as the complexity increases linearly with the number of multipath signals, inconvenient parameter updating and long operation time, a GPU based GNSS multipath signal parallel simulation method is proposed. Based on Farrow variable fractional delay filter, an independent parallel computing model of multi-channel filter coefficients is established, and a large number of floating-point units in GPU are used for parallel computing and filtering. Aiming at the performance bottleneck of this method, the optimization strategy of asynchronous parallel execution is given. The computer simulation results show that the complexity and operation time of the proposed method will not increase with the increase of the number of multipath signals. For the GNSS direct signal with sampling rate of 75MHz and time length of 1s, the maximum time consuming of the proposed method to generate 50 ∼ 1000 multipath signals is 158.6ms. Compared with the method of serial simulation on CPU, the proposed method speeds up to about 9.5 times, and the real-time simulation of multipath signals in complex environment can be realized.
An Improved Multi-target Tracking Algorithm for Automotive Radar
Authors: Huang Fan , Zhou Jianjiang , Zhao Xiaotong 
Published: July 2021

Abstract
An improved multi-target tracking algorithm for automotive radar is proposed in this paper. In the application of automotive radar, in addition to the motion generated by the target maneuver, the maneuver of the radar-equipped vehicle will also cause the target to move relative to the radar. A single motion model for the target cannot accurately describe the motion state of the target relative to the radar. To solve the above problems, the nonlinear state equations of various motion models and their process noise covariance matrix are derived, and the Interacting Multiple Model (IMM) algorithm, the Joint Probabilistic Data Association (JPDA) algorithm and the Unscented Kalman Filter with Doppler measurement (DUKF) are combined. At the same time, the correlation coefficient of range measurement error and velocity measurement error of Frequency Modulated Continuous Wave (FMCW) radar is derived, and the relationship between it and FMCW radar parameters is studied. By simulation, its influence on target tracking is assessed, final results indicate the reference value for the application of automotive radar in intelligent driving.
Theoretical derivation and law research on electrostatic charge of human walking
Authors: Chen Dong , Hu XiaoFeng , Xie XiNing , Meng DongXu , Du Xue 
Published: July 2021

Abstract
Electrostatic charging and discharging have an important impact on the safety and reliability of electronic products, and human static electricity is one of the important sources of electrostatic hazards in the production and use of electronic products. In order to analyze the electrification law of human walking and explore the electrostatic protection measures of human body, the theoretical model of human walking electrification is further deduced on the basis of previous studies. The electrification law of human walking under typical conditions is analyzed, and the influence of maximum foot lifting height on potential is studied. When the maximum lifting height is small, the potential oscillation approximately conforms to the sine law, and with the increase of the maximum lifting height, the sine law is no longer satisfied. When the maximum foot lifting height is small, the actual peak potential cannot reach the saturation potential. Therefore, on the basis of taking electrostatic protection measures, reducing the foot lifting height when walking is helpful to prevent electrostatic hazards.
Edge Computing in Smart Hydropower Station: Cloud-Edge Collaborative Framework
Authors: Luo Yuanlin , Zheng Bo , Wu Yuechao , Lu Jiong 
Published: July 2021

Abstract
With the deepening of energy revolution, smart hydropower station (SHS) has become the demand and inevitable direction of technological revolution and upgrading development of hydropower industry in China. Under the background of power Internet of Things and SHS, the massive sampled data perceived by intelligent terminals of SHS poses a major challenge to the traditional cloud computing centralized processing mode. As an important supplement to cloud computing, edge computing has received extensive attention from the power system. According to the complementarity between edge computing and cloud computing, this paper proposes a cloud-edge collaborative framework based on the introduction of SHS. Secondly, orienting the application in SHS, the key technologies of cloud-edge collaborative are discussed. Finally, the solutions based on cloud-edge collaborative for two typical application are discussed.
Research on emotion recognition algorithm based on improved BP neural network
Authors: Wang Qingqing , Wang Jing , Zhu Meili 
Published: July 2021

Abstract
The application of small robots in the production of freeze frame animation can speed up the production progress and improve the quality of production. Small robots will play the role of intelligent agents. It is necessary to recognize their emotions for the promotion of animation films. With the development of intelligent technology, BP neural network has been applied to the field of emotion recognition because of its advantages of self-learning, self-adaptive and self-organization. In this paper, the parameters of BP neural network are adjusted and improved to avoid the defects of BP neural network in emotion recognition. Then, the parameters and structure of the network are designed according to the emotion category. Then a three-layer BP neural network with 4 input nodes, 13 hidden layer nodes and 4 output nodes is constructed. Finally, it is applied to emotion recognition, and 10 groups of data are selected from the training samples for detection, and the diagnostic accuracy is 80%.
Research on distribution center layout optimization based on genetic algorithm
Authors: Zhang Siqi , Zheng Yiming , Li Guanyang 
Published: July 2021

Abstract
As the core of logistics system, distribution center is not only beneficial to the construction of modern logistics environment, but also to the realization of the overall benefits of logistics system. In order to improve the operation efficiency and have no limitations, we need to design the distribution center as a whole. In this paper, the system layout planning (SLP) combined with genetic algorithm is used to optimize the distribution center. In the use of system layout planning to get the comprehensive relationship between the functional areas, combined with the relevant constraints, build the objective function model, using MATLAB to run the genetic algorithm. The feasibility and rationality of the model are verified by an example. In this paper, the distribution center function area layout problem is regarded as a mathematical optimization problem, and the application of genetic algorithm in the model improves the quantifiable accuracy.
Automatic detection of thyroid nodules with ultrasound images: Basing on semi-supervised learning
Authors: Wang Qingsong , Zheng Jie , Yu Hui , Zhang Jingqi , Zhang Jie 
Published: July 2021

Abstract
Ultrasonography is one of the most common method for the diagnosis of thyroid nodules clinically. But the lack of experienced radiologists has become the biggest problem which reduce the accuracy and effectiveness of thyroid nodules’ detection. What’s more, although artificial intelligence (AI) has been widely used to solve the problem, the performance is limited by the size of the database. In this paper, an improved U-Net is designed for the segmentation of thyroid nodules, following a fully convolution network for the classification of thyroid nodules. During the training, pseudo-label is used for semi-supervised learning. The whole work is trained in 2032 ultrasound images (1000 nodules) and tested in 400 ultrasound images (200 nodules). The average dice coefficient is 89.7% in the test set for segmentation, while the precision and recall is 93.2% and 96% separately for classification. These results are higher than the method which only use the images with true labels, proving the effectiveness of pseudo-label.
Visual detection of eggs based on deep learning for egg picking robot
Authors: Xu Zhihong , Fan Mingyang , Zhang Yapeng 
Published: July 2021

Abstract
The chicken farm is a typical labor-intensive production environment, and the mechanization of egg picking work is one of the development directions of the chicken industry. This article uses a camera as a sensor for visual detection. Given the limited computing resources of the robot, we improve the feature extraction part of Mask R-CNN network to reduce the memory loss of parameters and speed up the detection process. The experimental results show that compared with the classic method and the Mask R-CNN basic algorithm, the method in this paper has a higher recognition rate that can better support egg picking robots in egg recognition and pose estimation.
Emotion recognition of musical instruments based on convolution long short time memory depth neural network
Authors: Wang Jing , Wang Qingqing , Liu Hongyan 
Published: July 2021

Abstract
In this paper, a method of emotion recognition for musical instruments based on convolution long short time memory depth neural network is proposed, and an emotion recognition music database composed of four musical instruments is established, including keyboard instruments, wind instruments, string instruments and percussion instruments. The emotional types of these four instruments are divided into happiness, anger, sadness and fear. Through the establishment of CLDNN model based musical instrument emotion recognition architecture, MFCCs, CNN and CFS are used for feature extraction and training. The experimental structure shows that the best classification effect is to use long-term memory (LSTM) and deep neural network (DNN) to extract and combine the emotional feature sets of musical instruments, which has the highest accuracy. Considering the dynamic changes of musical features in musical instruments, the modeling method is used to predict the emotional changes of musical instruments.
Interval hybrid judgment priority vector method in group decision
Authors: Liu Feier , Li Junhong 
Published: July 2021

Abstract
The uncertainty and subjectivity of the judgment matrix given by the experts according to the evaluation will directly affect the rationality of the priority vector. The interval hybrid judgment matrix can objectively reflect the preference information of experts. Therefore, combined with the theory of maximum satisfaction, CMIHMSM and CMIHMSM are established, and a consistency index is proposed to test the consistency of the matrix. On this basis, group decision making is introduced. Based on the cluster analysis and the consistency of the matrix, expert weights are divided into inter-class weight and intra-class weight calculations, so that expert weight under the same consistency of the same type of expert judgment matrix can be distinguished. Thus, the group priority vector is obtained. Example shows the feasibility and operability of the model.
Research on pricing strategy in urban parking management
Authors: Wen Haoyu , Yang Xiaoxia , Chen Xinyun 
Published: July 2021

Abstract
At present, many cities in China have a serious shortage of parking spaces, and it is necessary to find ways to alleviate this problem, including ways to make full use of limited parking resources, such as formulating dynamic pricing strategies. In this paper, the roadside parking with high turnover rate and high mobility in static traffic is selected as the research object. The classic bottleneck model is used to establish the roadside parking bottleneck model (OSPB) to study the dynamic pricing of roadside parking stations during peak hours. Based on the model comprehensively considering the time when the vehicle arrives at the parking station, the geographic location of the station and many other factors, the dynamic charging problem of roadside parking stations during peak parking hours is studied to improve the efficiency of parking space utilization. This model expands the application of the bottleneck model in static traffic, and in practice uses a large amount of historical parking data to verify the model.
Multi-radar collaborative networking method based on T-GCN
Authors: Liu Peng , Xu Yuheng , Ma Jing , Liang Jun , Xiao Han , Cong Yang 
Published: July 2021

Abstract
Multi-radar collaborative networking is the development direction and research hotspot of radar detection technology. In recent years, with the rise of artificial intelligence, researchers have introduced neural networks and other artificial intelligence methods into multi-radar collaborative networking and achieved remarkable results. In this work, we abstract the radar network as an un-weighted graph, and then introduce the GCN, and combine the characteristics of the GRU with the temporal context, apply the Temporal Graph Convolutional Network to multi-radar collaborative network. We solve the problem of low efficiency and slow speed of radar network without data fusion.
Multi-orthogonal signal underwater navigation system modeling on moving platform
Authors: Zhou Chenglin , Zhang Sen , Liu Jiahuan , Wang Mingzhi 
Published: July 2021

Abstract
When the Multi-Orthogonal Signal Underwater Navigation System(MOSUNS) works on a moving platform, the movement of the platform will bring some errors to the positioning of the MOUNS. To solve this problem, a motion error model is established based on the homogeneous coordinates and the homogeneous rotation matrix, and the relationship between the positioning error and the Rotating velocity of the moving platform (roll rate, pitch rate, yaw rate) is accurately described. The motion error model is verified by the simulation of the MOSUNS on the motion platform through MATLAB.
A testability modeling method based on structure-function-state-test
Authors: Wang Peng , Li Xingxin , Yu Yongli , Liu Chenguang , Luo Xibin 
Published: July 2021

Abstract
Aiming at the testability analysis of multi-state system, a testability modeling method based on structure-function-state-test is proposed. Based on the hierarchical structure analysis of the system, taking the function modeling of the structure as the core, the performance, fault and state of the function are described, and the test correlation of the system fault and state is studied. Through the analysis of a typical case, the results show that the model can: 1. Realize the system fault test correlation analysis considering the uncertain factors; 2. Realize the state test correlation analysis of the multi state system; 3. Realize the testability analysis of the multi state system by calculating the fault detection rate, fault isolation rate and state detection rate.
Configurations of tunnel deceleration area
Authors: Zhang Yanhong , Fan Yunlong , He Jianbin , Tian Jialei , Ma Xiangnan , Yuwen Weiyuan , Tan Yuanxin 
Published: July 2021

Abstract
The deceleration area of the tunnel has always been the place where accidents occur frequently. Recently published statistics on tunnel accidents showed that 42 percent of which are rear-end collisions. The main reason lies on the luminance difference between the inside and the outside parts of the tunnel. The inside part of the tunnel is much darker than that of the outside part, which results in a “sight shock” and shortage of clear sight for drivers at the tunnel portal. If the tunnel deceleration area is not long enough and the vehicle is at a high speed, rear-end collisions will most likely occur at this area. Therefore, it is important to design the deceleration area of the tunnel in order to avoid rear-end collisions. This paper explains the cause of rear-end collision at deceleration area from the principles of physics and physiology. The mathematical relations among the speed, time and distance of trucks and cars entering the tunnel are given from the mathematical point of view. Finally, this paper puts forward the design idea and reasonable suggestions for the configuration of tunnel deceleration area.
Optimization substrates and implantation metals for high performance photoacoustic laser streaming
Authors: Ai Xin , Qiu Yunao 
Published: July 2021

AbstractOptical manipulation of fluid has been widely investigated for applications in microfluidics, and photoacoustic laser streaming provides a promising technique because of its strong driving ability and flexibility as a microfluidic pump. However, except for the gold and quart, other substrates and metals have never been investigated in the fabrication of laser streaming pump. In this work, we demonstrate the effect of substrate and metal types on the performance of laser streaming. The Au-implanted quartz pump exhibit higher initial flow speed than Fe, while the durability of Fe-implanted plate is longer. The quartz substrate has a similar initial flow speed to glass, but much better endurance under long-time laser irradiation. In addition, the flow speed of incident laser with gradually increased power will be much higher than the direct high laser power. This study demonstrates the effective laser streaming of different substrates and implantation metals, thus paves the ways for optimizing the performance of photoacoustic streaming pump.
Research on the perceived risk of pesticide residues and consumption intention of fresh fruits and vegetables
Authors: Liu Yangyang , Ge Luping 
Published: July 2021

Abstract
The situation of pesticide residues in fresh fruits and vegetables was analyzed, and the necessity of the study was proposed; the factors influencing the risk perception of pesticide residues were analyzed, and the risk perception level questionnaire was designed and implemented, and the results showed that the higher the age, income, education level and living standard, the higher the risk perception level of pesticide residues; from the perspective of pulling the food consumption of people with high literacy and high income, strengthening the Internet media’s social orientation and supervision responsibilities, and suggesting the government to participate in the supervision and management of the whole food production process, three aspects are proposed to improve the willingness to consume and promote consumption.